Sequence( 0, 23.99, 1/5 ) Dynamic simulation periods are specified in Time's definition. This is usually a list of numbers or labels, typically in some unit of time (days, weeks, months, etc.). Use the ÒDynamic()Ó function in your variables to perform dynamic simulation. 2,339,108,476,224 1000 0 [] [1,2,3,4,5,6,7,8,9,10] 0 1 4 98 1 2 0 1 2 0 This model is a decision analysis in a poorly studied area, trip aggregation, and it studies decisions of two different stakeholders, the passenger and the society. In composite traffic, a centralised system collects the information on all trips online, aggregates the trips with the same origin and destination into public vehicles with eight or four seats, and sends the travel instructions to the passengers' mobile phones. We show here that in an urban area with one million inhabitants, this system could reduce environmental and other pressures of car traffic typically by 50-70 %, would attract about half of the car passengers, and within a broad operational range needs no public subsidies. Composite traffic gives a new level of freedom in urban decision-making towards solving the problems of urban traffic. The model is built using Analytica 3.0(TM) program that utilises a graphical interface for creating probabilistic (Monte Carlo) models. A free browser can be downloaded from the Analytica web site http://www.lumina.com . The file format for the models is XML, and therefore the code can also be viewed with a regular web browser. In this material, we present the main views of the graphical model and describe several modules in more detail. The model consists of two parts: a deterministic trip aggregation model that produces the output tables used in decision analysis. The calculation of the results takes several days and therefore they are stored as static tables in the module 'Static nodes'. In the second part the aggregation results are combined with cost functions, emission factors and other uncertain and/or varying variables using probabilistic (Monte Carlo) simulation. This part of the model is readily available for detailed examination, and several input values can be changed and explored using the Analytica Browser. Note, however, that the model (depending on dimensions used) easily requires more than 1 GB of RAM memory. 28.12.2005 Jouni Tuomisto The difference to version 1.0.1 is that there is a bunch of new scenarios. The old scenarios are still included, however. jtue (Jouni Tuomisto) 7. Novta 2002 13:32 jtue 16. syyta 2008 10:59 48,24 1,53,24,871,616,21 2,10,87,476,467 Arial, 13 0,Model Composite_traffic_v_,2,2,0,1,N:\Huippuyksikko\Tutkimus\R79_CompositeTraffic2\Mallit\CompositeTraffic_1_0_6.ana 81,1,1,0,2,1,4900,6400,7 2,40,7,450,720 1 656,512,1 52,44 65535,65532,19661 Composite_traffic_re 28.6.2004 Jouni Tuomisto This module contains brief description about pyrkilo diagram method in Analytica platform. There are explanations for node usage and colours. Version 5, 28.6.2004. Copyright KTL (National Public Health Institute, Finland). mtad 16. Aprta 2003 12:56 jtue 29. Junta 2004 10:55 48,24 56,149,0 48,29 1,1,1,1,1,1,0,0,0,0 1,40,8,418,558,17 Arial, 13 81,1,1,0,2,9,4744,6798,7 Contains data that comes from a referrable source. The reference must be mentioned in the Reference attribute. Colour 3R1B. 1 168,336,1 48,24 2,102,90,476,516 65535,52427,65534 Contains data that comes from a non-referrable source, i.e. some general knowledge or author judgement. Colour 4R2B. 1 168,392,1 48,24 52425,39321,65535 Contains information about general issues related to the structure and content of a model. Text is written to Description. Each addition is started with the date and the name of the user. The title of the node is Loki n or Log n (n=version number of the model). You should not write information related to a particular node, that should be written in the node itself so that the information will be inherited with the node. Colour 4L3B. 1 56,464,1 48,12 65535,54067,19661 Argument about a node, data, or relationship in a model; or a description of its importance. Colour: automatic (2L3B). 1 280,280,1 48,24 65535,31131,19661 This is the basic building block of an Analytica model. It is a variable that defines a (typically) measurable entity. Usually it is calculated based on data on and relationships about its causes. 1 168,280,1 48,24 Modules are used to create a hierarchical structure. Modules may contain nodes and other modules inside them. mtad 16. Aprta 2003 12:56 48,24 56,416,1 48,24 1,40,0,-2648,430,17 A conclusion is basically an argument. The colour is used to enhance the fact that the data for this argument originates from the results of the model. Colour 6L3B. 1 280,392,1 48,24 2,44,90,476,224 65535,65532,19661 Index related to the node beside it. Indexes should be as close as possible to the place where they are used. Otherwise there is the risk of a connection brake. Colour: automatic (5R2B). [0] 56,316,1 48,12 2,341,157,476,224 Colour description: xLyT describes the coordinates in the colour palette, xth cell from left and yth cell from top. Directions are L left, R right, T top, B bottom, e.g. 1R1B is the right bottom cell. 464,312,-1 112,56 Decision mode defines a decision under analysis. Other decisions (such as those decided by someone else) can be defined as uncertain variables instead of decisions. 0 56,280,1 48,24 Outcome of interest. The optimisation of this variable is often defined as the criteria for choosing between decision options. 0 56,360,1 48,24 An uncertain variable that is defined as a probability distribution. 0 168,448,1 48,24 This node describes the use of attributes. All attributes should be used as described in the manuals of Analytica. Exceptions are described here. This description is written for those who read as well as for those who write pyrkilo diagrams. Class: Shows the type of the node. Some of the variable classes have a special meaning in pyrkilo diagrams. These are presented in Help for pyrkilo diagrams module. Identifier: Name of the node that is used in definitions. This must be unique. When drafting a model, use automatic identifiers. Only when there is no expectation of many changes in node Titles, you can streamline all identifiers. Try to use short names and the same structure as in the Title. E.g. if title is Long-range transport, use identifier Lrt. Units: The unit of the value calculated in the node. Should always be defined when the node is a part of causal diagram. If the variable described by the node is dimensionless, '-' should be used. If an explicit unit is not (yet) defined, a description about how the variable could be measured should be used. E.g. 'mass', 'rate', 'age'. Title: The title that is shown on the node in diagram mode. First use a working name that roughly describes the contents. After your model is stabilised, you can rename nodes to better represent the final essence. Try to avoid intermediate nodes where it is difficult to understand what the outcome means; instead, combine consequent nodes so that the outcome is an understandable (measurable) variable. Description: A free-text area for describing the contents of the node. All nodes should be explicitly described and justified in the Description so that a user is able to get an idea of the node without looking at the Definition. If the definiton changes remarkably between versions, it is good to describe this and use dates and user's name for clarity (in a similar manner as in Log nodes). Definition: This defines what is calculated in this node. If the node is a non-numerical argument, it should contain a formula that refers to all nodes that are used to justify the claim; it this case, the numerical result will be nonsense. Pay attention to indexes: all important indexes must be included, but try to work with as few as possible. Use the ';' operator to chop the syntax into pieces. Check: Criteria to check the values entered. Reference: This contains a detailed information about the source of the data. The article or book should be found with this data. When building a model, there should be an accompanying RefMan file with the same name but a different extension. This file contains the full information about each reference cited in the model. The name and path of the RefMan file must be mentioned in the Reference of the model. If there is a relevant internet page or a network file, its path can be added here. Analytica 3.0 understands html-code and makes these links clickable. E.g. this is a link to the <a href=http://www.lumina.com>Lumina home page</a>. You can download Analytica Player from there to browse these models. 0 736,272,1 48,24 2,236,80,581,552 mtad 30. Aprta 2003 10:22 jtue 7. Mayta 2003 10:26 48,24 736,416,1 48,24 1,1,1,1,1,1,0,0,0,0 1,40,10,-2688,679,17 Arial, 13 79,1,1,0,2,9,4744,6798,7 0 56,448,1 48,24 1,1,1,1,1,1,0,,0, 65535,52427,65534 0 160,576,1 48,24 1,1,1,1,1,1,0,,0, 65535,52427,65534 0 160,392,1 48,24 1,1,1,1,1,1,0,,0, 65535,52427,65534 D_+R_+W_ 160,448,1 48,24 2,102,90,476,507 B_ 160,512,1 48,24 1,1,1,1,1,1,0,,0, 2,102,90,476,224 65535,52427,65534 Q_+Model_variable 264,448,1 48,24 0 552,424,1 64,40 65535,52427,65534 0 680,576,1 64,38 65535,52427,65534 0 680,352,1 48,28 49151,49151,49151 D__hoek_et_al_2002__+R__if_the_effect_was+W__design_was_approp 680,424,1 48,24 19661,48336,65535 B__greenland__cohort 680,488,1 52,24 65535,31131,19661 Hoek et al (Lancet 2002) found out in a cohort study that a higway near home was a risk factor for overall and cardiopulmonary mortality. The study was well designed and performed, and the results are convincing, althought the risk estimates were large. However, the exposure estimation was based on the vicinity of a major road and black smoke and nitric dioxide ambient concentrations. The current knowledge tends to associate especially fine particles (PM) with cardiopulmonary mortality, and in this study the exposure measure clearly was an indicator of traffic-related PM. However, other explanations exist as well, and e.g. noise was not ruled out as a confounder. Taken together, the study increases the plausibility of PM being causally linked to cardiopulmonary mortality. Q__therefore_probabl+Pm_plausibility 792,424,1 48,40 2,412,106,476,224 65535,31131,19661 0 576,136,1 48,28 52425,39321,65535 0 688,232,1 48,55 65535,52427,65534 0 688,56,1 48,38 49151,49151,49151 D__harry_is_born_in_+R__unless_harry_was_+W__because_b__those_ 688,136,1 48,29 1,0,-23 19661,48336,65535 Q__therefore_certain 800,136,1 48,24 65535,31131,19661 probability 0.9 904,424,1 48,24 2,102,90,476,224 52425,39321,65535 0 376,448,1 48,24 19661,48336,65535 Here we show how to describe a complicated reasoning starting with different kinds of data and ending up with a claim. This follows the ideas of Stephen Toulmin (Uses of Argument, Cambridge University Press, 1958). In a simple case or drafting phase it is enough to create an argument by joining a piece of data to the claim. A claim node is often called an Argument node, but it should be remembered, that a full argument includes the reasoning in addition to the outcome. Instead of using several nodes to describe the reasoning, as is done here, it is often convenient to write it down inside the argument node, in description attribute (see 'C: Traffic PM increases cardiopulm mortality'). Note that the arrows usually point from the model to the claim. The logic of this is that the model is seen as a description of reality. And the reality affects the claim, not vice versa. - Data (D) or premises describes the information that supports the claim. - Warrant (W) makes the argument from data to claim a legitimate one. Often it is not explicitly mentioned in discussion, but it should be described in a pyrkilo unless it is obvious. - Backing (B) contains some background information needed for warrant, and often it is practical to combine these two. - Qualifier (Q) describes the strength of the argument. - Rebuttal (R) defines the scope when the argument is valid. 256,180,-1 248,172 jtue 28. Junta 2004 18:03 48,24 736,472,1 48,29 1,257,40,-2679,515,17 The set module is used as a set containing items, such as a set of emission sources summing up to the total emission. However, it is not possible to refer to a module. Therefore a set node is used instead of the module for refering in the model. If there are items that belong to several sets, aliases are created and placed in each set module. A set node is located in the respective set module. Colour 9L3T. jtue 30. Octta 2003 10:34 48,24 56,144,1 48,24 1,60,78,-7060,477,17 1,52427,26212 This node is like an alias to a set module. The set module is used as a set containing items, but it is not possible to refer to a module. Therefore a set node is created, and it is used instead of the module for refering in the model. If there are items that belong to several sets, aliases are created and placed in each set. Set node is located in the respective set module. Colour 9L3T. Item1+Item2+Item3+Item4+Item5 48,24,1 48,24 1,724,97 1,52427,26212 This is one item belonging to the set 'Set'. 0 168,24,1 48,24 This is one item belonging to the set 'Set'. 0 168,248,1 48,24 This is one item belonging to the set 'Set'. 0 168,192,1 48,24 This is one item belonging to the set 'Set'. 0 168,136,1 48,24 This is one item belonging to the set 'Set'. 0 168,80,1 48,24 Shows that there is a need for a model, but it is not yet defined explicitly. Colour 7L2B. Model prototype has the same meaning, either can be used depending on whether a node or a module is more convenient. Colour 7L2B. 1 56,88,1 48,24 52429,65535,39321 Shows that there is a need for a model, but it is not yet defined explicitly. Colour 7L2B. Placeholder for a model has the same meaning, either can be used depending on whether a node or a module is more convenient. Colour 7L2B. mtad 16. Aprta 2003 12:56 48,24 56,32,1 48,24 52429,65535,39321 A question or a lack of knowledge. An item that would be important in a model, but there is no information about its value and it cannot therefore be used in calculations. Colour: 5R1T. 0 56,272,1 52,28 49151,49151,49151 Note: colour depends on the source of information and can be that of original data, author judgement or causal node. Do NOT use the automatic colour (L2B3), because it is reserved for the argument. 0 168,256,1 48,24 65535,52427,65534 (param1) 0 168,32,1 48,24 param1 168,88,1 48,24 2,356,138,476,224 Shows the strength of an argument (or more precisely, the strength of the data, warrant, and backing to support the argument). There may be several pieces of data connected by several qualifiers to one argument (claim). Colour: automatic (8R3B). 0 168,144,1 48,24 This is used to emphasize that there is a lack of knowledge about this variable, but the variable is still important for understanding causality. In a well-built model, the nuisance parameters are cancelled out before the outcome, so that they do not affect the result given the data used. E.g., we may want to model the human exposure to traffic emissions. We have an estimate about the relative contributions of the most important sources to the exposure, but we don't have absolute values from fate and dispersion models. Therefore, when we want to express causal connections, we must 0 56,208,1 48,29 58981,65535,52427 Non-causal nodes are used when the relation between nodes is not causal. Ice cream consumption and drowning accidents are correlated (at least in Finland). One is not a cause of the other, but both are affected by the outside temperature during summer. Non-causal connections can be used in models for deducting values for one node when the other is known. A typical example of this is a top-down model, where we may have information on 1) total concentration of a pollutant and 2) source contribution of certain emission sources (as a fraction of total concentration). The source contribution is not a causal variable, it is merely an index of all (unknown) causal variables, but it can be used to estimate the concentration caused by a particular emission source. 0 168,200,1 48,24 2,587,271,476,412 39321,55707,65535 29.10.2003 Jouni Tuomisto This is the third version of the Help model. Originally it was planned that it would habe been included in each model as a linked module, but soon it was found that the link caused much more trouble than benefit. It is therefore used as a stand alone model. However, it may be a good idea to embed it into each model using Copy (not Link). N:\huippuyksikko\tutkimus\mallit\TainioTheUsesOfArgument.ana (7.5.2003) is closed and connected to this file in the module 'Argument strucure'. It is edited to reflect the current thinking about the argument structure. 1 736,368,1 48,12 1,1,1,1,1,1,1,,0, 2,433,83,476,398 65535,54067,19661 Arial, 13 A value or preference. Colour 8L4B. 0 280,448,1 48,24 5,65535,1 Pyrkilo diagram method (or structured deliberation as it is sometimes called) has been developed to facilitate the Science-Policy Interface. There is a need for methods facilitating the flow of information and understanding between science and policy. The principle is to describe a risk situation in a formal manner. Pyrkilo is an enhanced causal diagram that contains items along a causal pathway (or network) from e.g. abatement strategies to emissions to dispersion to exposure to effects. It has been designed to describe also other than causal connections such as non-causal reasoning, values, preferences, and arguments. These diagrams use Analytica(TM) platform, a graphical Monte Carlo simulation program. It is based on nodes (or variables or objects). They are used to describe and define all the pieces needed for a description of the situation under scrutiny. Many nodes are used as described in Analytica manuals. However, there are also special colours and shapes representing features that are important for pyrkilo diagrams. See Description of each node for more details. You can see the definitions and descriptions by clicking or double-clicking the nodes. 408,124,-1 400,116 2,402,92,530,558 Understanding of a particular risk develops simultaneously at various levels and using differently structured methods. At one end there is a "discussion layer": political and public discussions about risks of a hazard with a wide interest on e.g. economical consequences of available decision options, public health, and social justification equity. This discussion is sometimes poorly structured and does not give scientists or risk assessors clear questions that could be answered by scientific methods. At the other end there is a "modelling layer": models dealing with specific questions such as air concentrations of a pollutant from a specific source or risk-benefit analyses of particular actions. The models are complex and use several assumptions that are not abvious to outsiders. There is a need for methods facilitating the flow of information and understanding between the existing layers. Pyrkilo (from the Finnish word pyrkiŠ, to aim at) diagrams aim at offering an interface between the two disciplines. The principle is to describe a risk situation in a formal manner. It is an enhanced causal diagram that contains items along the causal pathway (or network) from e.g. emissions to dispersion to exposure to effects. It has been designed to describe also other than causal connections such as values, preferences, and arguments. The pyrkilo method has several objecitves. The structure is relatively easily understandable and readable. It has a basic structure similar to but simpler than mathematical causal models. Translation into a natural language or mathematical model is relatively easy, as is required from an interface. The diagram is fast to build with little data, and the critical parts of the diagram can subsequently be developed into a full-scale model. It is easy to expand into new areas, as the political discussion proceeds. Because of its structured and formal nature, it requires that many assumptions are made explicit unlike in political rhetoric, and therefore it is easier to identify possibly illogical or conflicting issues. It can be used to explore the validity or importance of an aspect before it is brought into the other discipline of heavy modelling or political discourse. 0 736,328,1 48,24 2,54,111,476,224 A scope node is basically an argument. The bevel is used to enhance the fact that the argument is about the scope of the model, (i.e. about the existence of a node or module). Colour: automatic (2L3B). 1 280,336,1 48,24 1,1,1,1,1,1,0,,1, 65535,31131,19661 Traffic congestion in urban areas is rapidly becoming the most important obstacle for town development. In addition traffic is causing major environmental, health, and economical problems. On the other hand it is vital for the functions of the modern society. Pressure 224,128,1 48,46 2,102,90,476,357 Many people driving cars are not willing to use public transportation. This may be due to poor connections, difficult timing, uncomfort of changing etc. Pressure 224,232,1 60,52 2,50,301,476,372 Pressure; Effect 368,504,1 56,55 2,102,90,476,414 Steve Pye and Paul Watkiss: CAFE CBA: Baseline analysis 2000 to 2020. AEAT/ED51014/ Baseline Issue 2. <a href= "http://www.iiasa.ac.at/docs/HOTP/Mar05/cafe-cba-baseline-results.pdf" >Click</a> State; Effect 224,336,1 56,48 Pressure 224,472,1 76,80 Other_actions 368,176,1 48,24 Other_actions; Driving_force 368,232,1 48,24 [Constant Co2_emissions_must_b] Pressure 368,288,1 48,24 State 368,344,1 48,24 Exposure 368,400,1 48,24 [Constant Traffic_is_major_sou] Other_actions; Effect 656,232,1 68,64 65535,31131,19661 The maybe most important effect (and the most difficult to model) comes from the increased degree of freedom in urban policy-making. Some examples of the possible changes: The pressures towards enlarging road infrastructure are relieved, giving resources to other possible targets. Car limits in e.g. historical city centres can be implemented without disrupting peoples' possibilities to move freely in the city. Public transportation can be provided in areas where sparse population or poor urban planning hamper efficient bus service. It will become cheaper to implement technical measures to reduce emissions with a smaller, intensively used fleet. Many families can give up the second, and sometimes even the first car, when most trips can be performed without an own vehicle. Reduced pressures to buy an own car decrease problems of car-owning to city infrastructure. Elderly, disabled, and young people get more freedom to move around. Parents don't need to drive their children so much. The need for driving drunk reduces. The connection between the freedom to move and car ownership is loosened. Emission reduction techniques are more economic, as there are fewer vehicles that drive more. Even expensive solutions such as hydrogen or electricity may become profitable. The question of mileage per tank is not an issue with composite vehicles, which makes it easier to use electricity. Composite_traffic_re 504,512,1 68,48 2,379,84,520,386 65535,65532,19661 Driving_force 364,84,1 68,55 ktluser 8. maata 2005 6:30 48,24 504,232,1 48,24 1,76,97,988,564,17 We found out that with composite traffic 1. most trips are direct; 40% involve one change 2. most pressures reduce by 50-70% but driver salary costs are high 3. ca. 50 % of passengers found it cheaper 4. day-time traffic does not need subsidies composite_traffic_dummy 680,304,1 92,92 2,512,136,476,381 65535,65532,19661 We studied 1. how effectively trips can be combined 2. what are the various costs of each option 3. what is the variation of perceived costs among passengers 4. what incentives are needed to reach targets composite_traffic_dummy 680,104,1 88,89 1,1,1,1,1,1,0,,1, 2,102,90,476,473 jtue 24. Febta 2005 15:24 48,24 504,160,1 48,24 1,267,117,846,493,17 100,1,1,1,2,9,2970,2100,15 2,53,21,627,600 The best alternative for society is about 45-60% of current car traffic to change to composite traffic. The fraction is relative to the area of guarantee but is still rather robust. With evening trips, composite traffic is better only at high guarantee. In contrast, during night composite traffic is not competitive, and it is always more expensive than car traffic. However, the availability of composite traffic around the clock is an important factor when car-owners are considering not to buy a new car at all. This pheniomenon is not modelled here, but it is probably important. If composite traffic is subvented during nights, the overall societal costs are still well in favor of composite traffic. This is because night trips are not numerous and can easily be subsidised. Societal_cost 584,64,1 48,38 2,102,90,476,452 [Alias A45_60__composite_f2] 65535,65532,19661 We show here that in an urban area with one million inhabitants, this system could reduce environmental and other pressures of car traffic typically by 50-70 %, would attract about half of the car passengers, and within a broad operational range needs no public subsidies. Composite traffic gives a new level of freedom in urban decision-making towards solving the problems of urban traffic. Table_1_pressures 352,80,1 52,44 2,131,221,476,279 [Alias Composite_traffic_r1] 65535,65532,19661 This module calculates the actual trips, modes of transportation, and delays during trips and vehicle transfers. It also calculates the kilometres traveled by each type of vehicle and number of vehicles needed. The composite traffic trips are allocated into different vehicles. The following hierarchy is used in allocation. If the criterion is fulfilled, that number of passengers is allocated, and the rest will go to the next criterion. The criteria are used for a group of trips that has the same origin, destination, and time. Time resolution is 12 min. Origin and destination are described as '129-areas' used for city authorities in Helsinki metropolitan area. The 129 areas have on average 7300 inhabitants (0, 25%, 50%, 75%, and 100% percentiles are 0, 3400, 6800, 10300, and 28300, respectively). 1) Use an 8-seat vehicle if there are enough passengers to get it full. 2) Use a 4-seat vehicle if there are enough passengers to get it full. Divide the trips into two parts so that the passengers change vehicle in the most busy point along the route. Then, 3) Use an 8-seat vehicle if there are enough passengers to get it full. 4) Use a 4-seat vehicle if there are enough passengers to get it full. 5) Use a 4-seat vehicle for all remaining trips. The criterion is checked at the actual arrival time at the transfer point, i.e. the model takes into account the different travel times between areas. The following outputs are calculated: Number of passenger trips by mode (car or composite traffic) Number of passenger trips by vehicle type. Note that in this output, the trip that includes a transfer is calculated twice. Vehicle kilometres driven Parking lots needed for the vehicles that are used Average vehicle numbers per hour for the 30 most busy links at 8.00-9.00 in the morning Number of vehicles needed Waiting time due to traffic jams and waiting for composite vehicle to arrive. The outputs of each scenario are indexed (when relevant) by period (day, evening, night); zone (Helsinki downtown, other centre, suburb), length of trip (less or more than 5 km), and vehicle type (8-seat or 4-seat vehicle with of without transfer, or car). jtue 6. helta 2003 18:55 48,24 304,232,1 48,24 1,262,42,516,573,17 2,102,90,476,451 67,1,1,0,1,1,2794,1728,0 time units Travel time between two city areas. It includes the time that is spent in the composite vehicle when it drives within the origin or destination area picking up or dropping off other passengers. However, the travel times of composite vehicles and car are estimated to be so close to each other that the same value is used for both. (In any case, the resolution is 12 min anyway). ceil(Distances/Traffic_speed/time_unit) 168,56,1 48,24 2,262,247,476,324 2,414,130,694,363,0,MIDM [From,To1] passengers Size of vehicles that is used to allocate passengers into vehicles. For cars, the average number of passengers is 1.345 (See Car occupancy). A slightly higher number is used here, because with low volumes (1-4 passengers) the need of cars is overestimated if the actual number is used. Even if the higher number overcompensates this and causes bias, it is in favour of personal cars. Table(Vehicle)( 8,8,4,4,4,1.5) 280,56,1 48,24 1,1,1,1,1,1,0,,0, 2,9,108,476,406 2,88,98,416,303,0,MIDM 52425,39321,65535 [Hellman, 2004 54 /id] trips/time unit The composite traffic trips are allocated into different vehicles. The following hierarchy is used in allocation. If the criterion is fulfilled, that number of passengers is allocated, and the rest will go to the next criterion. The criteria are used for a group of trips that has the same origin, destination, and time. 1) Use an 8-seat vehicle if there are enough passengers to get it full. 2) Use a 4-seat vehicle if there are enough passengers to get it full. Divide the trips into two parts so that the passenger changes vehicle in the most busy point along the route. Then, 3) Use an 8-seat vehicle if there are enough passengers to get it full. 4) Use a 4-seat vehicle if there are enough passengers to get it full. 5) Use a 4-seat vehicle for all the remaining trips. The criterion is checked at the actual arrival time at a transfer point, i.e. the model takes into account the different travel times between areas. var v:= Transfer_point; var b:= All_trips[Mode1='Composite']; var h:= mod(b,vehicle_size[Vehicle='Bus no change']); var bus:= b-h; b:= h; h:= mod(b,vehicle_size[Vehicle='Cab no change']); var cab:= b-h; b:= h; var noch:= round(b*scenario_input[input_var='No-change fraction']); b:= b-noch; var a:= From&','&To1; var j:= if v=a then b else 0; b:= b-j; a:= ','&To1; {laskee alkumatkan matkasuoritteen} var d:= for x[]:= a do ( var c:= (if findintext(From&x,v)>0 then b else 0); c:= sum(c,To1) ); {siirtŠŠ matkasuoritetta alkumatkan viipeen verran.} var e:= selecttext(v,6,9); e:= for x[]:= evaluate(e) do delay[To1=x]; b:= time_shift(b,e); {laskee loppumatkan matkasuoritteen} a:= From&','; b:= for x[]:= a do ( var c:= (if findintext(x&To1,v)>0 then b else 0); c:= sum(c,From) ); b:= j+d+b; b:= b+noch; h:= mod(b,vehicle_size[Vehicle='Bus one change']); var bus1:= b-h; b:= h; h:= mod(b,vehicle_size[Vehicle='Cab one change']); var cab1:= b-h; var rix1:= h; var car:= All_trips[Mode1='Car']; array(Vehicle_noch,[bus,bus1,cab,cab1,rix1,car,noch]) 168,128,1 48,24 2,460,35,476,575 2,95,119,696,484,0,MIDM [To1,From] [Index Mista] vehicles Total number of vehicles needed to run the system. It is assumed that cars can be used in a similar way as composite vehicles, i.e. that if a car is parked, anyone can take and use it. This is of course unrealistic, but the bias is in the favour of car travelling. In addition, this number is not used for the final car need calculations. var a:= cumulative_balance; var driving:= -sum(a,from); a:= a-min(a,time); a:= sum(a,from)+driving; max(a,time) 280,272,1 48,24 2,518,118,476,305 2,43,183,670,431,1,MIDM [Time,Vehicle] [Index Travel_type] vehicles The highest number of vehicles during the observation period in each area. This excludes vehicles that are driving through the area. This is a proxy of parking lot need in the area. For practical reasons, the numbers are aggregated into zone level. It is assumed that cars can be used in a similar way as composite vehicles, i.e. that if a car is parked, anyone can take and use it. This is of course unrealistic, but the bias underestimates the parking lot need in favour of car travelling. It is also assumed that composite vehicles and cars use separate parking areas. In this way the beforementioned bias does not affect the estimate for composite traffic. var a:= cumulative_balance; var b:= a[Vehicle='Car']; a:= sum(a,Vehicle)-b; a:= array(Vehicle,[a,0,0,0,0,b]); a:= max(a,time)-min(a,time); var c:= zones[area1=from]; a:= if Zone=c then a else 0; a:= sum(a,from); a 56,208,1 48,24 2,25,35,476,460 2,-30,196,686,341,0,MIDM [Zone,Vehicle] [Index Region2] 36,1,1,0,1,9,6798,4744,7 vehicles/h The average number of vehicles per hour driving along a link for the 30 most busy links at 8.00-9.00 in the morning. Note that each street consists of two links going to opposite directions. average(link_intensity_per_name,link_intensity_per_name.link) 512,272,1 48,24 2,385,178,476,384 2,14,8,345,248,0,MIDM The 30 most busy links based on the scenario with cars only. index Top30:= 1..30; var b:= trips_per_link_BAU; b:= b[From=Floor(Link/10000),To1=(Link-Floor(Link/10000)*10000)]; b:= sortindex(-b,Link); var a:= 1..size(Top30); a:=slice(b,a); slice(a,Top30) 512,128,1 48,24 2,457,82,476,421 2,735,44,226,642,0,MIDM [To1,From] 1,I,4,2,0,0 vehicles/h The number of vehicles per hour driving along a link for the 30 most busy links at 8.00-9.00 in the morning. The result is indexed by the names of the areas that are connected by the particular link. var d:= basic_ranking; var mist:= floor(d/10000); var mihi:= d-floor(d/10000)*10000; var a:= Vehicles_per_link; a:= a[From=mist,To1=mihi]; d:= area_name[area1=floor(d/10000)]&' - '&area_name[area1=d-floor(d/10000)*10000]; index link:= d; var c:=cumulate(1,link); slice(a,a.top30,c) 512,208,1 48,24 2,129,54,476,566 2,46,12,824,709,0,MIDM [Index Travel_type] passengers/d The number of transfers (changing composite vehicle in the middle of a trip) in each area. var a:= sum(Trips[vehicle_noch=vehicle],Vehicle); a:= sum(a-sum(All_trips,Mode1),time); var fro:= sum(a,To1); var to:= sum(a,from); fro+to[to1=from] 56,56,1 48,24 2,109,186,476,425 2,781,43,296,405,0,MIDM [To1,From] trips/h Total number of trips travelled per hour in the whole area. var a:= Trips[vehicle_noch=vehicle]; a:= sum(sum(a,From),To1)/time_unit; a 56,128,1 48,24 2,637,66,476,410 2,25,49,676,547,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:7.25M Zminimum:1 Zmaximum:2 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] Arial, 8 [Vehicle,Time] vehicles Cumulative net balance of vehicles and its development in time. This could take into account the compensative gap filling, i.e. if there is shortage of composite vehicles, empty vehicles are transported into the area. However, in the current version, it is assumed that empty vehicles are not transported. Because of this, there must be enough vehicles in each area so that it will not run out of them at any time of the day. dynamic(0,Cumulative_balance[time-1]+Vehicle_balance) 168,272,1 48,24 2,102,90,476,369 2,178,119,756,399,1,MIDM [Time,Vehicle] [Index From] vehicles/time unit Number of vehicles coming to and leaving each area, i.e. the net balance of the area for each time point. var b:= ceil(Trips[vehicle_noch=vehicle]/vehicle_size); var a:= time_shift(b,delay+1); a:= sum(a,From); a:= a[To1=From]; a:= -sum(b,To1)+a; var bus:= a[Vehicle='Bus no change'] +a[Vehicle='Bus one change']; var cab:= a[Vehicle='Cab no change'] +a[Vehicle='Cab one change'] +a[Vehicle='Cab non-full']; var car:= a[Vehicle='Car']; array(Vehicle,[bus,0,cab,0,0,car]) 168,208,1 48,24 2,471,43,476,649 2,181,47,694,349,1,MIDM [Time,Vehicle] [Index From] vehicles/h The number of vehicles in each link. var v:= Route_matrix; var a:= From&','&To1; index e:= Sequence(8,8.99,time_unit); var g:= ceil(Trips[vehicle_noch=vehicle]/vehicle_size); g:= sum(g[time=e],e); {laskee matkasuoritteen joka linkille erikseen} var d:= for x[]:= a do ( var c:= (if findintext(x,v)>0 then g else 0); c:= sum(sum(c,From),To1) ); d 392,208,1 48,24 2,425,58,476,528 2,96,75,797,552,0,MIDM [To1,From] trips/h Vehicles per link in a scenario with cars only. This is used to rank the links according to their vehicle intensities. var v:= Route_matrix; var a:= From&','&To1; index e:= sequence(8,8.99,time_unit); var f:= sum(adjusted_trip_rate[time=e],e); for x[]:= a do ( var c:= (if findintext(x,v)>0 then f else 0); c:= sum(sum(c,From),To1) ) 512,56,1 48,24 2,102,90,476,354 2,74,10,797,552,0,MIDM [To1,From] km/time unit Number of vehicle kilometres driven during each time unit. var a:= ceil(Trips[vehicle_noch=vehicle]/vehicle_size); a:= aggr_period(a); var b:= array(Length_1,[0,1]); b:= if distances < 5 then 1-b else b; a:= b*a; sum(sum(a*Distances,From),To1) 280,128,1 48,24 2,368,50,476,445 2,529,11,591,291,0,MIDM [Period_1,Vehicle] [Sysvar Time] 88,1,1,0,2,9,4744,6798,7 min Calculates the waiting time for composite traffic. First, we calculate the number of vehicles running between each points at each time. This is calculated for short (< 5 km) and long trips separately. We assume that the vehicles run at relatively regular intervals, and then the expected waiting time is half of the time difference between the vehicles. Then we sum over areas and aggregate over time, and calculate the trip-number-weighted waiting time. var a:= if Vehicle='Car' then 0 else trips[vehicle_noch=vehicle]; a:= sum(ceil(a/vehicle_size),Vehicle); var e:= array(Length_1,[0,1]); e:= if distances < 5 then 1-e else e; var b:= ceil(time_unit*60/a/2); var c:= for x:= waiting_time do ( var d:= if b=x then a*e else 0; d:= sum(sum(d,From),To1); aggr_period(d)); c:= sum(c*waiting_time,waiting_time)/sum(c,waiting_time); array(Vehicle,[c,c*2,c,c*2,c*2,0]) 400,56,1 48,24 2,530,58,474,601 2,11,131,416,303,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:1 Zminimum:0 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] [Period_1,Vehicle] [Index J] A dummy index 1..12 400,88,1 48,12 The areas are classified into three categories: 1) downtown (downtown of Helsinki), 2) centre (other major centres within the Metropolitan area), and 3) suburb (all other areas). Table(Area1)( 1,1,1,1,2,2,2,2,2,2,2,2,3,3,2,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,2,2,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,3,3,3,3,3,3,3,2,3,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,0) 56,272,1 48,24 2,782,11,215,614,0,MIDM 52425,39321,65535 trips Total composite and car trips classified into zones and periods. This is the number of original trips, which is divided into car and composite trips. Compare Trips.v by zone. var a:= aggr_period(All_trips[Mode1='Composite']); var b:= aggr_period(All_trips[Mode1='Car']); a:= array(Vehicle,[a,0,0,0,0,b]); var c:= array(Length_1,[0,1]); c:= if distances < 5 then 1-c else c; a:= sum(a*c,From); b:= zones[area1=to1]; a:= if Zone=b then a else 0; a:= sum(a,to1); a 56,336,1 48,24 2,670,35,476,371 2,253,263,718,295,0,MIDM [Zone,Vehicle] The combined result of various variables using the basic assumptions. This output is copied to the module 'Static nodes' and subsequently used as the basis for cost calculations. var a:= array(Output_1,[0,0,0,0,link_intensity,total_vehicle_need,0]); a:= if Length_1='< 5 km' then a else 0; a:= if periods=1 then a else 0; a:= a+array(Output_1,[0,0,vehicle_km,0,0,0,waiting]); a:= if Zone=1 then a else 0; var b:= if Length_1='< 5 km' and periods=1 then areal_vehicle_peak else 0; a:= a+array(Output_1,[Trips_m_by_zone,0,0,b,0,0,0]); a:= a[vehicle=vehicle_noch]; a:= if a = null then 0 else a; a+array(Output_1,[0,trips_v_by_zone,0,0,0,0,0]) 400,272,1 48,24 2,622,28,476,522 2,760,134,487,304,0,MIDM [Alias Bau_scenario_output1] Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:1 Zminimum:0 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] [Period_1,Vehicle_noch] [Index Length_1] The length of the trip classified as short (< 5 km) and long. ['< 5 km','>= 5 km'] 280,160,1 48,12 ['< 5 km','>= 5 km'] We should also look suboptimal aggregation (non-full vehicles) BEFORE dividing the trips into two parts. However, this is not easy as we do not know which trips can be aggregated into full vehicles before we have already divided the trip into two parts. I would say that this has little practical significance, as with large traffic volumes, the non-full vehicles are a minority. If, however, the need to transfer appeared to be a major hindrance of using composite traffic, this could be a method to increase direct connections. trips 88,432,1 64,63 2,102,90,476,392 trips Total composite and car trips classified into zones and periods. This is indexed by different vehicle types based on the modelled allocation. Note that this number is greater than Trips.m by zone, because here all trips with transfer are calculated as two separate trips. var a:= aggr_period(trips); var c:= array(Length_1,[0,1]); c:= if distances < 5 then 1-c else c; a:= sum(a*c,From); c:= zones[area1=to1]; a:= if Zone=c then a else 0; a:= sum(a,to1); a 248,336,1 48,24 2,102,90,476,388 2,455,302,450,295,0,MIDM [Zone,Vehicle_noch] vehicles/time unit Number of vehicles coming to and leaving each area, i.e. the net balance of the area for each time point. var b:= ceil(Trips[vehicle_noch=vehicle]/vehicle_size); var a:= time_shift(b,delay+1); a:= sum(a,From); a:= a[To1=From]; a:= sum(b,To1)-a; var bus:= a[Vehicle='Bus no change'] +a[Vehicle='Bus one change']; var cab:= a[Vehicle='Cab no change'] +a[Vehicle='Cab one change'] +a[Vehicle='Cab non-full']; var car:= a[Vehicle='Car']; a:= array(Vehicle,[bus,0,cab,0,0,car]); sum(a,from) 280,208,1 48,24 2,102,90,476,384 2,280,25,694,349,1,MIDM [Time,Vehicle] vehicles Total number of vehicles needed to run the system. It is assumed that cars can be used in a similar way as composite vehicles, i.e. that if a car is parked, anyone can take and use it. This is of course unrealistic, but the bias is in the favour of car travelling. In addition, this number is not used for the final car need calculations. var a:= cumulative_balance; var driving:= -sum(a,from); a:= a-min(a,time); a:= sum(a,from)+driving; max(a,time); a 376,352,1 48,24 2,43,183,623,292,1,MIDM [Time,Vehicle] This module calculates various pressures of different traffic scenarios. The estimates are based on Outputs node (which has been calculated beforehand due to slow calculations) and the numbers are stored in Static nodes). The outputs of each scenario are indexed (when relevant) by period (day, evening, night); zone (Helsinki downtown, other centre, suburb), length of trip (less or more than 5 km), and vehicle type (8-seat or 4-seat vehicle with of without transfer, or car). Costs are separately calculated for the passenger and the society. Some costs affect these stakeholders differently, such as fine particle and carbon dioxide emissions: they are calculated as societal costs only, not as costs to a passenger. The following endpoints are considered (see Table 1): Fraction of composite trips without change (%) Vehicles needed (number) Parking places need (number) Average vehicle flow on the 30 most busy roads (vehicles/h at 8.00-9.00 AM) Fine particle (<2.5 µm of diameter) emissions (kg per day) Carbon dioxide emissions (ton per day) Driver salaries (thousand e per day) Vehicle capital and operational costs (thousand e per day) Time cost (thousand e per day) Average car trip cost to passenger (e per trip) Expected composite trip cost to passenger (e per trip) The following costs are taken into account for passenger (P) or societal (S) costs: Vehicle capital cost (P+S) Driver salary cost (P+S) Driving cost (fuel) (P+S) Parking (parking fees for individual drivers) (P) Parking land (opportunity cost of reserving land to parking purposes) (P+S) Emissions (fine particles and carbon dioxide causing health and climate change effects, respectively (S) Time for waiting composite vahicles, time spent in traffic jams (P+S) Accidents (an option only, not used in the current model) Ticket (profit for composite service provider) (P) The module has a submodule Cost elements. It contains the detailed descriptions of the unit costs and other input variables that are used to calculate the pressures of each scenario. The values used are dependent on the stakeholder. For example, the car price is the price that a random new car would cost, and it has therefore large uncertainty. On the other hand, the price of a 4-seat composite vehicle is the average price a taxi-style car would cost in Finland, and the confidence intervals are narrower because there is no individual uncertainty. This is because the price of an individual car affects the costs of individual car trips, while the cost of composite trip is dependent on the total cost of vehicles. Variation between individuals has been separately estimated for three variables: how passengers evaluate the capital costs of owning a car; how passengers are willing to pay for either the right to drive themselves or to not need to drive; and how many passengers are traveling together. jtue 6. syyta 2004 13:46 48,24 424,232,1 48,24 1,124,20,614,695,17 # or #/h A set of scenarios organised along two indexes: Guar is the level of composite traffic guarantee. This means that trips within a certain area will be organised by composite travel, while areas outside this guarantee remain without the service. The point in using this index is to explore whether composite traffic can be started with low profile and expanded geographically as more people start using it. Comp_fr is the fraction of trips that were previously performed by personal car but are performed by composite traffic in the scenario. var a:= Scen[vehicle_noch=vehicle]; var b:= Scenarios1_0_1[.scenario=a.scenario]; a:= if b[input_var='Composite fraction']=Comp_fr_1 then a else 0; a:= if b[input_var='Guarantee level']=guar then a else 0; a:= if Comp_fr_1=0 then a[guar=7] else a; a:= a[guar=choose_guar]; a:= if Comp_fr_1=0 and Output_1='Waiting' then 0 else a; a:= a[Comp_fr_1=choose_comp]; a:= if b[input_var='Flexible fraction']=choose_flexible then a else 0; a:= if b[input_var='No-change fraction']=choose_nochange then a else 0; a:= if b[input_var='Large guarantee?']='Yes' then (if large='Yes' then a else 0) else (if large='No' then a else 0); a:= a[large=choose_large]; a:= sum(a,a.scenario); a 176,32,1 48,24 2,578,30,527,620 2,77,213,1061,433,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:5 Xmaximum:15 Yminimum:0 Ymaximum:1M Zminimum:1 Zmaximum:6 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1, 1, 1, 1, 1, 0, 0, 0] Probindex:[5%, 25%, 50%, 75%, 95%] Arial, 5 [Comp_fr_1,Vehicle] [Index Vehicle] [0,0,0,0] e/d The total cost (per day) of various cost elements calculated for each vehicle type separately. var park:= if Vehicle='Car' then sum(Car_parking_cost,Zone) else 0; var land:= sum(Parking_land_cost,Zone); var emiss:= sum(Emission_cost,emission); {var tim:= sum(time_cost,time_cost.i);} array(Cost_structure_1,[Vehicle_cost,Driver_cost,Driving_cost,park,land, emiss,time_cost,acc_costs,0]) 304,216,1 48,24 2,535,61,476,624 2,14,49,754,524,0,MEAN Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:7.25M Zminimum:1 Zmaximum:2 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1, 1, 1, 1, 1, 0, 0, 0] Probindex:[5%, 25%, 50%, 75%, 95%] Arial, 6 [Cost_structure_1,Vehicle] [Index Length_1] [0,0,0,0] The various costs that are included in the model. The details of each cost are described in the respective node in the 'Detailed costs' module. Accidents are omitted, although there is a placeholder. ['Vehicle','Driver','Driving','Parking','Parking land','Emissions','Time','Accidents','Ticket'] 304,248,1 52,12 2,102,90,476,466 2,15,262,416,303,0,MIDM [0,1,1,0] ['Vehicle','Driver','Driving','Parking','Parking land','Emissions','Time','Accidents','Ticket'] e/trip The costs calculated per trip. These numbers are not yet weighted by stakeholder-specific weights (Cost strength), and therefore eg. the driver costs for car trips is high (the assumption is that all car drivers get full salary). var a:= transport_cost[vehicle='Car']; a:= array(mode1,[a,sum(transport_cost,vehicle)-a]); var b:= trips_per_period; a:= if Cost_structure_1='Vehicle' or Cost_structure_1='Parking land' then sum(a,Period_1)/sum(b,Period_1) else a/b; a:= a[Period_1=choose_period]; a:= if Cost_structure_1='Ticket' and Mode1='Composite' then ticket-group_subvention else a; a 304,296,1 48,24 2,52,74,476,436 2,165,80,756,459,0,MEAN [Flexible_fr,Cost_structure_1] 1,D,4,2,0,0 [Index Length_1] [0,0,0,0] The variation of how much an individual values the capital costs of the personal car when estimating the costs of a single trip. If the person needs the car only for trips within the composite traffic area, the valuation might be 1. However, the car is often needed for other purposes also such as longer trips (value: <1), and some people like to own a car in any case (value: 0). ktluser 24. lokta 2004 12:48 ktluser 28. lokta 2004 23:31 48,24 56,304,1 48,24 1,1,1,1,1,1,0,0,0,0 1,103,163,425,387,17 Arial, 13 fraction Each row represents one possibility for the distribution of individual valuations in the population. Probability distributions are used to represent this variation within population. Table(Self)( Uniform(0,1),Triangular(0,0,1),Bernoulli(0.2)) [1,2,3] 56,32,1 48,24 2,376,89,476,280 2,540,439,269,145,0,MIDM 2,293,348,291,265,0,SAMP 65535,52427,65534 [Self,Self,Undefined,Undefined,1,2] [0,0,0,0] Based on author judgement, as there is no data available. The uncertainty between several valuation distributions on the population level. Probtable(Self)( (1/3),(1/3),(1/3)) 56,96,1 48,24 2,144,331,416,303,0,MIDM 2,248,258,416,303,0,SAMP 52425,39321,65535 [1,2,3] [0,1,0,0] Based on author judgement, as there is no data available. fraction The aggregate of the car capital variation and uncertainty. Cap_variab_2[Cap_variab=Cap_uncert] 56,160,1 48,24 2,247,96,476,420 2,1032,316,222,303,0,SAMP [1,0,0,0] fractile The fractile of the sample within the population. average(sample(Cap_variab_2),cap_variab) 168,32,1 48,24 2,199,80,476,224 2,714,7,271,303,0,SAMP [Run,Cap_variab] [1,0,0,0] 9.5.2005 Jouni Tuomisto Vanha syntaksi, ennen kuin keksin miten epŠvarmuus ja vaihtelu erotetaan: var a:= rank(cap_variab,run)/samplesize; a[Cap_variab=Cap_uncert] var a:= sample(cap_variab); a:= a[run=sortindex(a,run)]; var b:= sample(uniform(0,1)); a[run=sortindex(b,run)] 168,96,1 48,24 2,421,613,387,206 2,606,345,360,275,0,SAMP Probindex:[0.05, 0.25, 0.5, 0.75, 0.95 ] Statsselect:[1, 1, 1, 1, 1, 0, 0, 0 ] Includezzero:0 Includeyzero:0 Includexzero:0 Yintervals:0 Xintervals:0 Zmaximum:3 Zminimum:1 Ymaximum:1 Yminimum:0 Xmaximum:100 Xminimum:0 Showkey:1 Frameauto:1 Depth:70 Tilt:0 Rotation:45 Scales:1 Mesh:1 Ticks:1 Grid:1 Frame:1 Linestyle:10 Baroverlap:0 Symbolsize:6 Cdfsteps:1 Cdfresol:5 Diststeps:1 Distresol:10 Graphtool:0 Arial, 8 [Cap_variab,Cap_variab,Undefined,Undefined,Undefined,2] [1,0,0,0] The price that the passenger is willing to pay to be able to drive the vehicle him/herself compared with the situation where the composite driver drives the vehicle. Note that for car passengers, the question is not about driving but being a passenger in a car or in a composite vehicle. There are also cases where the car driver is not traveling but only chauffeuring passengers that do not have driver's license. The willingness to drive is probably low in these cases, but we were very modest in these estimates. There exists no data about this variable, because it is about a comparison between the current and a hypothetical situation. Author judgement is therefore used. ktluser 24. lokta 2004 12:48 48,24 56,360,1 48,24 1,1,1,1,1,1,0,0,0,0 1,135,92,568,333,17 Arial, 13 fraction Willingness to drive. This is expressed as fraction of composite driver's salary. Each row represents one possibility for the distribution of individual valuations in the population. Probability distributions are used to represent this variation within population. Table(Self)( Uniform(-0.3,0),Triangular(-0.1,0,0.3),Uniform(-0.2,0.2)) [1,2,3] 64,40,1 48,24 2,102,90,476,405 2,967,354,275,231,0,MIDM 2,651,20,316,276,0,SAMP 65535,52427,65534 [Self,Self,Undefined,Undefined,1,2] [0,0,0,0] Based on author judgement, as there is no data available. The uncertainty between several valuation distributions on the population level. Probtable(Self)( (1/3),(1/3),(1/3)) 64,104,1 48,24 2,248,258,416,303,0,SAMP 52425,39321,65535 [1,2,3] [0,1,0,0] Based on author judgement, as there is no data available. fraction The aggregate of the willingness to drive variation and uncertainty. It is expressed as a fraction of composite driver's salary. Drive_variab_2[Drive_variab=Drive_uncert] 64,168,1 48,24 2,366,255,416,303,1,CDFP fractile The fractile of the sample within the population. average(sample(drive_variab_2),drive_variab) 176,40,1 48,24 2,104,69,476,224 2,984,22,260,280,0,MIDM [Run,Cap_variab] [0,0,0,0] var a:= sample(drive_variab); a:= a[run=sortindex(a,run)]; var b:= sample(uniform(0,1)); a[run=sortindex(b,run)] 176,104,1 48,24 2,576,48,377,441,0,SAMP Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:100 Yminimum:0 Ymaximum:1 Zminimum:1 Zmaximum:3 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] Arial, 8 [Cap_variab,Run] trips/period Number of trips per period var a:= sum(scenarios_output[Output_1='Trips'],Zone); a:= if Mode1='Composite' then a else 0; a:= if Vehicle='Car' then (if Mode1='Car' then a[Mode1='Composite'] else 0) else a; a:= sum(a,Vehicle); a 56,216,1 48,24 2,102,90,476,257 2,20,201,446,428,0,MIDM [Length_1,Comp_fr_1] [Index Length_1] e/trip The cost per trip for a random individual passenger. These values have been weighted by the stakeholder-specific weights (Cost strength). The costs are first calculated for an average trip from total costs and total numbers of trips. The costs of individual car trips depend on the number of passengers. Therefore, the average cost is multiplied by the average number of passengers and divided by the number of passengers in the particular case we are looking at. var a:= mean(Group_size)/sample(Group_size); a:= if Cost_structure_1 <>'Time' and Mode1='Car' then a else 1; a:= a*cost_per_trip; a:= if isnan(a) then 0 else a; a:= a*cost_strength; sum(a,Cost_structure_1) 304,360,1 48,24 2,468,14,515,590 2,133,66,833,363,0,MEAN Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:100 Yminimum:0 Ymaximum:9 Zminimum:1 Zmaximum:2 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] Arial, 2 [Stakeholder_1,Mode1] [Index Cost_structure_1] [0,0,0,0] There are three different stakeholders: 'Passenger' is a random sample of passengers who have chosen the personal car in the business-as-usual scenario, and may choose between car and composite traffic in other scenarios. 'Society' the community that is responsible for the well-being of citizens in the metropolitan area. It also has the ability to pay subsidies to public transportation. Societal costs include other costs than passenger costs, such as health effects of air pollution, and opportunity costs of parking space. 'Bus company', the composite traffic service provider, is a simple stakeholder and does not therefore show up in the stakeholder index. Its only interest (in the model) is to get a reasonable profit ('Ticket' cost) from each composite trip (in addition to covering direct costs). ['Passenger','Society'] 176,392,1 48,12 ['Passenger','Society'] This module contains the detailed descriptions of the unit costs and other input variables that are used to calculate the pressures of each scenario. The values used are dependent on the context. For example, the car price is the price that a random new car would cost, and it has therefore large uncertainty. On the other hand, the price of a 4-seat composite vehicle is the average price a taxi-style car would cost in Finland, and the confidence intervals are narrower because there is no individual uncertainty. This is because the price of an individual car affects the costs of individual car trips, while the cost of a composite trip is dependent on the total cost of vehicles to the service provider. ktluser 3. marta 2004 16:37 48,24 176,296,1 48,24 1,158,13,100,505,17 [Alias Cost_elements1] g/km Fine particle and carbon dioxide unit emissions for average vehicles. Fine particle emissions are taken from the Lipasto model using average (mixed gasoline and diesel) values for personal car and diesel EURO3 (applied since 2000) values for composite vahicles. For CO2, typical emissions of a new car were used based on the Finnish Vehicle Administration AKE. The following vehicles are used as typical examples of the class: 8-seat vehicle: Toyota Hiace 2.5 D4D 100 4 door long DX bus 4-seat vehicle: Toyota Corolla 2.0 90 D4D Linea Terra 5 door Hatchback (diesel) Car: Toyota Corolla 1.6 VVT-i Linea Terra 5ov Hatchback (gasoline) var a:= triangular(0.3,1,1.7); var b:= triangular(0.9,1,1.1); Table(Vehicle,Emission)( 0.1*a,232*b, 0.1*a,232*b, 0.1*a,153*b, 0.1*a,153*b, 0.1*a,153*b, 0.047*triangular(0,1,2),168*triangular(0.9,1,1.1) ) 192,88,1 48,24 2,45,51,618,623 2,545,214,416,303,0,MIDM 2,56,66,416,303,0,MEAN 65535,52427,65534 [Emission,Vehicle] [Emission,Vehicle] http://lipasto.vtt.fi/yksikkopaastot/henkiloautotkeskimaarin.htm PŠŠkaupunkiseudun julkaisusarja B1999: 5. Vaihtoehtoisten polttoaineiden kŠyttšmahdollisuudet joukkoliikentessŠ PŠŠkaupunkiseudulla. Taulukko 3, Keskusta ja esikaupunki. Autorekisterikeskus AKE: Uuden auton kulutustiedot. EKOAKE, huhtikuu 2003. ['PM','CO2'] 192,120,1 48,12 e/vehicle Price of a new vehicle. Note that the interpretation is slightly different with different vehicles. The car price is the price that a random new car would cost, and it has therefore large uncertainty. The price of a composite vehicle is the average price of a taxi-style car in Finland, and the confidence intervals are narrower because there is no individual uncertainty. This is because the price of an individual car affects the costs of individual car trips, while the cost of a composite trip is dependent on the total cost of vehicles to the service provider. var a:= 39.52K*Triangular(0.75,1,1.25); var b:= 22.6K*Triangular(0.75,1,1.25); var c:= lognormal(19.1K,1.5); array(vehicle,[a,a,b,b,b,c]) 56,32,1 48,24 2,102,90,476,375 2,22,50,416,303,0,MIDM 2,68,58,839,549,1,PDFP 65535,52427,65534 Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:-20K Xmaximum:80K Yminimum:-1u Ymaximum:1u Zminimum:1 Zmaximum:6 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] a Expected operation time of a new vehicle. var a:= 7*Triangular(0.75,1,1.25); var b:= 5*Triangular(0.75,1,1.25); var c:= 9*Triangular(0.7,1,1.3); array(vehicle,[a,a,b,b,b,c]) 56,88,1 48,24 2,102,90,476,484 2,14,383,416,303,0,MIDM 65535,52427,65534 l/km Fuel consumption of a vehicle. It is assumed that composite vehicles use diesel fuel and cars use gasoline. The values are based on standardised European consumption values of a new car. var a:= (8.7/100)*Triangular(0.75,1,1.25); var b:= (5.7/100)*Triangular(0.75,1,1.25); var c:= (8/100)*Triangular(0.5,1,1.5); array(vehicle,[a,a,b,b,b,c]) 56,160,1 48,24 2,454,28,476,455 2,425,410,416,303,0,MIDM 2,152,162,416,303,1,PDFP 65535,52427,65534 e/l Diesel price for composite vehicles; gasoline price for cars. The values are based on rough follow-up of retail prices in Finland in fall 2004 - summer 2005. var a:= 0.95*triangular(0.8,1,1.2); var b:= 1.22*triangular(0.8,1,1.2); array(Vehicle,[a,a,a,a,a,b]) 56,216,1 48,24 2,102,90,476,529 2,481,182,416,246,0,MIDM 2,264,274,697,303,1,PDFP 65535,52427,65534 [0,0,0,0] St1 gas station, Kuopio keskusta, 6.9.2004. e/h Monthly salary and social security costs (35 %), and scaled to one hour assuming 160 hours of work per month. The salary is based on that of bus drivers in municipality-owned bus companies. var a:= 2313/160*1.35; normal(a,a*0.18) 192,32,1 48,24 2,102,90,476,468 2,411,332,416,303,0,CONF 65535,52427,65534 [0,0,0,0] Statistics Finland 2005 <a href= "http://statfin.stat.fi/StatWeb/start.asp?LA=en&lp=home&DM=SLEN" >Click</a> e/d/parking space Cost of a parking space to the society due to the opportunity loss of the land, and maintenance costs. var va1:= 1.05^30; var a:= 20*3000; a:= (a-a/va1)*va1; a:= a/30/365; a/2*lognormal(1,1.3) 192,272,1 48,24 2,102,90,476,328 2,40,50,416,303,0,MIDM 65535,52427,65534 [0,0,0,0] e/kg Assumptions: Primary fine particle emissions of 24290 kg/a caused 12.5 deaths in a risk assessment study in Helsinki (Tainio et al, 2005). We here use the distribution of deaths per emission derived from that study. The value of a statistical life is 0.98-2 Me (Watkiss et al., 2005). The official value for road economy calculations is 201.879 e/kg (LVM, 2003). This value is within the range derived from Tainio, but clearly lower than the mean. CO2 emission price comes from the emission trade market. According to Helsingin Sanomat (7 May, 2005), it was 18 e/ton in 5 Apr, 2005, although it had been lower during previous months. In July, it was approaching 30 e/ton according to Taloussanomat. The official value for road economy calculations is 32 e/ton (LVM, 2003), which is within the range used here. var a:= Pm_unit_lethality; array(Emission, [a*uniform(0.98M,2M), uniform(5,40)/1000]) 192,216,1 48,24 2,73,7,548,645 2,466,127,416,303,0,MIDM 2,54,148,672,472,1,PDFP 65535,52427,65534 Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:1 Zminimum:0 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] [0,0,0,0] Tainio, M., Tuomisto, J.T., HŠnninen, O., Aarnio, P., Koistinen, K.J., Jantunen, M.J., and Pekkanen J. Health effects caused by primary particulate matter (PM2.5) emitted from buses in the Helsinki Metropolitan Area, Finland. Risk Analysis, Vol. 25, No.1, 2005. pp. 151-160. {[Tainio, 2005 96 /id]} <a href="http://www.blackwell-synergy.com/links/doi/10.1111/j.0272-4332.2005.00574.x/abs/">Link to publisher</a> {[Watkiss, 2005]} <a href="http://europa.eu.int/comm/environment/air/cafe/activities/cba_baseline_results2000_2020.pdf">Click</a> {[LVM, 2003]} <a href="http://www.mintc.fi/www/sivut/dokumentit/julkaisu/mietinnot/2003/b292003.pdf">Click</a> trips/d/car Number of trips per car per day, i.e. the cumulative number of passenger that use the car during the day. This value is used to calculate the need of cars. uniform(4,10) 312,88,1 48,24 65535,52427,65534 e/trip The income the service provider wants to get from composite traffic users in addition to the price of the direct costs (vehicle, fuel, driver, and parking costs). Uniform( 0.2, 0.6 ) 312,160,1 48,24 65535,52427,65534 passengers Size of group traveling together for a random passenger. var a:= Car_occupancy*occupancy; a:= a/sum(a,occupancy); chancedist(a,occupancy,occupancy) 192,328,1 48,24 2,355,136,476,344 2,445,95,416,473,0,MEAN 65535,52427,65534 An index for the number of passengers in a personal car. 1..5 56,360,1 48,12 h, fraction Delay that is caused by increased link intensity. The node contains two values. Delay is the average time of delay due to traffic jams during daytime. Reduction is the relative reduction to 'Link intensity' (average vehicle flow on the 30 most busy roads at 8.00-9.00 AM) that is needed to reduce the delay to 0 min. Table(Self)( (Triangular(0,0,10)/60),0.3) ['Delay','Reduction'] 312,32,1 48,24 2,102,90,476,386 2,592,87,416,303,0,MIDM 2,40,50,416,303,0,SAMP 65535,52427,65534 [Self,Run] [0,0,0,0] e/trip The cost of 30 min parking in zones 1, 2, 3 in Helsinki. It is assumed that each car trip involves 30 min of parking during daytime, while during evening and night, the parking is free. Also daytime parking at home is included in these estimates, although it is difficult to valuate. In any case, it is common to pay at least 5-10 euro per month for a parking place (or more for a garage), which is 15-30 cents per day. Due to the uncertainties, the confidence intervals are large. Table(Period_1,Zone)( ((2.4*0.5)*Triangular(0,1,2)),((1.2*0.5)*Triangular(0,1,2)),((0.6*0.5)*Triangular(0,1,2)), 0,0,0, 0,0,0 ) 312,272,1 48,24 2,102,90,476,392 2,336,267,416,303,0,MIDM 2,232,242,416,303,1,PDFP 65535,52427,65534 [Period_1,Zone] cases/a The number of injuries and deaths in traffic accidents in Vantaa, Espoo, and Helsinki, respectively. It is assumed that the number of 2002 or 2003 statistics is the expectation. Poisson distribution is used to describe the uncertainty. Taulukko 1-1 Liikenneonnettomuudet Vantaalla v. 2002 YhteensŠ Hvo Ovo Loukkaantui Kuoli Auto-onnettomuus 570 100 470 155 5 MoottoripyšrŠonnettomuus 23 15 8 13 2 Mopo-onnettomuus 14 6 8 7 0 PolkupyšrŠonnettomuus 47 37 10 40 0 Jalankulkijaonnettomuus 33 29 4 31 0 YhteensŠ tieliikenne 687 187 500 246 7 Raideliikenne (jk) 8 8 - 1 7 Hvo= henkilšvahinkoon johtanut onn. Ovo= omaisuusvahinkoon johtanut onn. LIIKENNEONNETTOMUUDET VUONNA 2003 Pelti rytisi Espoon alueella viime vuonna yhteensŠ 434 kertaa. Henkilšvahinko-onnettomuuksia oli 135, niissŠ kuoli 3 ja loukkaantui 159 henkilšŠ. Edelliseen vuoteen verrattuna liikenneonnettomuuksien mŠŠrŠ kŠŠntyi hienoiseen laskuun. Vuonna 2002 tilastoitiin 538 onnettomuutta. Liikenneonnettomuustiedot on koottu poliisille ilmoitetuista onnettomuustapauksista. Onnettomuuskustannukset Liikenneonnettomuudet aiheuttivat HelsingissŠ vuonna 2003 yhteensŠ 244 miljoonan euron yhteiskunnalliset kustannukset. Henkilšvahinkoihin johtaneiden onnettomuuksien osuus oli 213 miljoonaa euroa. Laskelma perustuu liikenne- ja viestintŠministerišn hyvŠksymiin liikenneonnettomuuksien yksikkškustannuksiin vuodelta 2000. Kustannuksissa ovat mukana onnettomuuksien aiheuttamat reaalitaloudelliset menetykset ja ns. hyvinvoinnin menetys. Taloudellisia kustannuksia ovat sairaanhoitokulut, uhrin tyšn menetys, ajoneuvovahingot sekŠ muut aineelliset vahingot. Table(Self)( Poisson(((246+159)+724)),Poisson(((7+3)+16))) ['Injuries','Deaths'] 312,408,1 48,24 2,82,80,500,500 2,578,153,416,303,0,MIDM 2,136,146,416,303,0,STAT 65535,52427,65534 Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:1 Zminimum:0 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] [0,0,0,1] Liikenneonnettomuudet Vantaalla 2002. C21:2003. Vantaan kaupunki, Vantaa 2003. <a href="http://www.vantaa.fi/i_liitetiedosto.asp?path=1;135;137;221;1761;1827;7348;7349">Internet PDF</a> Liikenneonnettomuudet HelsingissŠ vuonna 2003. <a href="http://www.hel.fi/ksv/hela/Kaupunkisuunnittelulautakunta/Esityslistat/liitteet/041670240.pdf">Internet file</a> Espoon kaupunki, liikenneturvallisuus. <a href="http://www.espoo.fi/xsl_taso2_alasivuilla.asp?path=1;606;607;4214;7808">Internet page</a> http://www.tieh.fi/liikenneturvallisuus/lion04.pdf e/d The societal costs of traffic accidents were 227 million euro in Helsinki in 2004. For the whole metropolitan area, this is more than 1 million euro per day. The numbers are scaled up from Helsinki to the metropolitan area based on the numbers of injured people in accidents. The uncertainty is based on the standard deviation of the variable Accidents (deaths), which is ca. 20% of the mean. The accident cost number for Helsinki is scaled up by the number of injuries in the whole Helsinki Metropolitan Area (for data and references, see Accidents). "Onnettomuuskustannukset Liikenneonnettomuudet aiheuttivat HelsingissŠ vuonna 2003 yhteensŠ 244 miljoonan euron yhteiskunnalliset kustannukset. Henkilšvahinkoihin johtaneiden onnettomuuksien osuus oli 213 miljoonaa euroa. Laskelma perustuu liikenne- ja viestintŠministerišn hyvŠksymiin liikenneonnettomuuksien yksikkškustannuksiin vuodelta 2000. Kustannuksissa ovat mukana onnettomuuksien aiheuttamat reaalitaloudelliset menetykset ja ns. hyvinvoinnin menetys. Taloudellisia kustannuksia ovat sairaanhoitokulut, uhrin tyšn menetys, ajoneuvovahingot sekŠ muut aineelliset vahingot." var a:= 227M*((246+159+724)/724)/365; normal(a,a/5) 312,328,1 48,24 2,511,78,500,544 2,26,124,416,303,1,PDFP 65535,52427,65534 [0,0,0,0] Liikenneonnettomuudet HelsingissŠ vuonna 2003. <a href="http://www.hel.fi/ksv/hela/Kaupunkisuunnittelulautakunta/Esityslistat/liitteet/041670240.pdf">Internet file</a> Liikenneonnettomuudet HelsingissŠ vuonna 2004. <a href="http://www.hel.fi/ksv/Mita_suunnitellaan/Liikenne/tilastoja/liikenneonnettomuudet2004.pdf"> Internet file </a> http://www.ytv.fi/FIN/seutu_ymparistotietoja/liikkuminen/onnettomuudet/etusivu.htm The costs of car have large individual variation. This might be an important factor in the comparison of car and composite traffic. This is not currently done but could be considered in the future versions of the model. Fuel_consumption; Vehicle_lifetime; Vehicle_price; 0 464,48,1 48,29 The costs are calculated for a passenger who has a car in the household and is trying to decide between the car and composite traffic. vehicle_price 464,160,1 68,72 1,1,1,1,1,1,0,,1, 2,102,90,476,427 [Alias The_costs_are_calcu1] e/h The cost of time spent waiting for a composite vehicle or in traffic jam. Triangular( 0, 5.9, 11.8 ) ['Delay','Reduction','Cost'] 312,216,1 48,24 2,102,90,476,301 2,199,277,416,303,0,MIDM 65535,52427,65534 e/trip This subvention is given to passengers that travel in groups with more than one person. The idea is that the subsidy is an amount (uncertain to the decision-maker) which is given to everyone in the group except the first one. In this way, the total group subsidy increases with the size of the group (just like the efficiency of car travelling increases with more passengers). We assume here that the groups are identical in both car and composite modes. var a:= uniform(0,2); a:= a*(sample(Group_size)-1)/sample(Group_size); if subsidise_groups_='Yes' then a else 0 192,408,1 48,24 2,144,229,512,326 2,136,146,416,303,0,MIDM 65535,52427,65534 [Run,Subsidise_groups_] Personal car becomes more efficient if there are several passengers. To attract groups to use the composite traffic, it is possible to subsidise groups so that there is a certain reduction in the ticket price. This node determines whether group subsidies are considered in the model or not. In the default model, this variable is set to No. Choice(Self,2) 56,408,1 48,24 2,102,90,476,342 [Formnode Subsidise_groups_1] ['Yes','No'] fraction Proportion of cars with different number of passengers. The last number is divided into occupancy '4' and '5' based on author judgement. The original data is from streets entering downtown Helsinki during a weekday (from 6.00 to 21.00) in May. driver 72.0 % driver+1 passenger 23.3 % driver+2 passengers 3.3 % driver+ at least 3 passenger 1.4 % var a:= array(occupancy,[0.72,0.233,0.033,0.01,0.004]); a 56,328,1 48,24 2,102,90,476,345 2,445,95,416,473,0,MIDM 65535,52427,65534 e/km Maintenance costs (service, tyres, oil etc.). This is based on Autoliitto's report 'Costs of car 2004'. Insurance and use tax are excluded, as like capital costs, there may be other reasons to own the car, and then these would be sunken costs. Original values assuming an old car with the original price 20000 e, 20000 km/a of driving (e/a): Maintenance 844 Tyres 320 total 1164/20000 = 0.0582 e/km Triangular( 0.03, 0.058, 0.086 ) 56,272,1 48,24 2,210,329,416,303,0,MEAN 65535,52427,65534 deaths/kg Assumptions: Primary fine particle emissions of 24290 kg/a caused 12.5 deaths in a risk assessment study in Helsinki (Tainio et al, 2005). We use the distribution of deaths per emission derived from that study. var a:= fractiles([ -7.223e-004, 5.640e-006, 4.228e-005, 5.987e-005, 8.013e-005, 1.150e-004, 2.037e-004, 2.939e-004, 3.598e-004, 4.132e-004, 4.640e-004, 5.139e-004, 5.662e-004, 6.233e-004, 6.854e-004, 7.577e-004, 8.441e-004, 9.519e-004, 1.093e-003, 1.314e-003, 2.805e-003]); a 192,160,1 48,24 2,102,90,476,428 2,466,127,416,303,0,MIDM 2,54,148,672,472,1,PDFP 65535,52427,65534 Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:1 Zminimum:0 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] [0,0,0,0] Tainio, M., Tuomisto, J.T., HŠnninen, O., Aarnio, P., Koistinen, K.J., Jantunen, M.J., and Pekkanen J. Health effects caused by primary particulate matter (PM2.5) emitted from buses in the Helsinki Metropolitan Area, Finland. Risk Analysis, Vol. 25, No.1, 2005. pp. 151-160. {[Tainio, 2005 96 /id]} <a href="http://www.blackwell-synergy.com/links/doi/10.1111/j.0272-4332.2005.00574.x/abs/">Link to publisher</a> The fraction of trips that were previously performed by personal car but are performed by composite traffic in the scenario. [0,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1] 176,64,1 48,12 2,460,148,476,416 2,236,315,416,303,0,MIDM [0,0,0,0] The level of composite traffic guarantee. This means that trips within certain areas will be organised by composite travel, while areas outside this guarantee remain without the service. The point in using this index is to explore whether composite traffic can be started with low profile and expanded geographically as more people start using it. [1,2,3,4,5,6,7] 176,88,1 48,12 [0,0,0,1] You can choose which composite fraction(s) is (are) calculated. If you choose All, you will get a more thorough result, but it will take more memory and computation time, especially if 'Choose guar' or 'Choose period' are also All. Choice(Comp_fr_1,0,True) 56,32,1 48,16 2,40,50,416,382,0,DEFA [Formnode Choose_comp1] 52425,39321,65535 ['item 1'] [Variable Choose_nochange] You can choose which guarantee level(s) is (are) calculated. If you choose All, you will get a more thorough result, but it will take more memory and computation time, especially if 'Choose comp' or 'Choose period' are also All. Choice(Guar,7,True) 56,64,1 48,16 2,-2,232,476,224 [Formnode Choose_guar1] 52425,39321,65535 ['item 1'] You can choose which period(s) is (are) calculated. If you choose All, you will get a more thorough result, but it will take more memory and computation time, especially if 'Choose guar' or 'Choose comp' are also All. Choice(Period_1,0,True) 416,296,1 48,16 [Formnode Choose_period1] 52425,39321,65535 ['item 1'] Detailed costs and pressures. See each individual node for a full description. ktluser 24. marta 2004 0:00 48,24 176,216,1 48,24 1,402,104,409,441,17 kg/d Total emissions based on kilometres driven. The unit emissions are based on standard values. var a:= sum(scenarios_output,Zone); a:= a[Output_1='Vehicle km']; a*Emission_factor/1000 64,192,1 48,16 2,218,232,476,224 2,43,56,717,317,0,MIDM [Vehicle,Period_1] [Index Travel_type] persons The number of full-time drivers needed in the composite traffic. This is based on the kilometres driven and an 8-hour working day. It is assumed that there is no waiting for drivers. This assumption probably causes underestimation of the true number. var a:= Scenarios_output[Output_1='Vehicle km']; a:= slice(a,region,1); ceil(a/traffic_speed/8) 176,304,1 48,16 2,102,90,476,286 2,19,38,868,392,1,MIDM [Comp_fr_1,Period_1] [Index Travel_type] vehicles For composite vehicles, this comes directly from traffic optimising; for cars, it is simply the number of trips divided by the average number of trips per car per day. For cars, the amount needed is difficult to estimate, because most cars are needed also for trips beyond the area modelled here. Therefore, even if some trips are performed by composite traffic, it is possible that the number of cars needed remains the same but the number of trips per car decreases. var a:= Trips_per_period[Mode1='Car']/Trips_per_car; var b:= trips_per_period[Mode1='Composite']; b:= b/sum(b,Length_1); b:= b*sum(sum(scenarios_output[Output_1='Vehicles'],Zone),Length_1); b:= if Vehicle='Car' then a else b; if periods=1 then b else 0 64,32,1 48,16 2,470,127,477,494 2,228,80,711,417,0,MIDM [Vehicle,Comp_fr_1] [Index Length_1] e/d It is assumed that each car trip involves parking. However, composite traffic does not pay anything in parking meters. Instead, they have to pay for the land. This cost is calculated as Parking land cost. scenarios_output[Output_1='Trips',Vehicle='Car']*parking_price 176,128,1 48,16 2,77,296,476,402 2,278,125,602,242,0,MIDM [Period_1,Zone] e/d Fine particles are assumed to cause 10 deaths per 17 ton emission, a result from buses in Helsinki (1). CO2 costs are based on the estimated costs of CO2 in the greenhouse gas emission market. The true health and environmental costs are probably clearly higher than the price of the emission market. emission1*Emission_unit_cost 176,192,1 48,16 2,102,90,476,392 2,301,50,639,305,0,MIDM [Vehicle,Emission] [Index Travel_type] e/d Cost of parking land. It is assumed that for composite vehicles, there is a fixed amount of reserved parking places. The cost is equal to the societal cost of the land use. This cost is allocated to short and long trips based on the number of trips. var a:= scenarios_output; var b:= a[Output_1='Trips']; b:= b/sum(b,Length_1); a:= sum(a[Output_1='Parking lot'],Length_1); a*Parking_space*b 176,160,1 48,16 2,541,90,476,285 2,457,12,555,416,0,MIDM [Zone,Vehicle] [Index Length_1] "The accident risk of taxies (related to kilometres driven) is 40 percent lower than that of regular drivers. However, the accident density is 10.4 accidents per year per 100 cars, is double the number for private drivers." .6 344,48,1 48,24 65535,52427,65534 Ammattiliikenteen turvallisuuden kehittŠminen. LINTU-projektin osaraportti 12. Research report 566/2000. VTT 2000, Espoo. <a href="http://www.vtt.fi/rte/projects/srs/raportit/lintu_osa12_ammattiliik.pdf">Internet PDF</a> e/d We assume that half of the accidents are attributable to personal car traffic, while the other half is attributable to other traffic modes (walking, cycling, public transportation). In addition, the accident risk is proportional to the change in traffic volume, but there is uncertainty about the slope. The expected value is that when traffic volume decreases 10%, accident risk decreases 5%; but it could vary between 0% and 10%. It is likely that these two assumptions underestimate rather than overestimate the benefit of composite traffic, but we were careful not to exaggerate the benefits. The guidelines for road projects #REF# assume that accidents are proportional to the traffic volume. var a:= sum(scenarios_output,Zone); a:= a[Output_1='Vehicle km']; var b:= sum(sum(sum(a,vehicle),Length_1),Period_1); b:= (1-b/b[Comp_fr_1=0])*triangular(0,0.5,1); b:= (1-b)*accident_costs*0.5; a:= a/sum(sum(sum(a,vehicle),Length_1),Period_1); b*a 464,104,1 48,24 2,523,131,476,433 2,52,9,714,303,0,MIDM [Period_1,Vehicle] [Index Length_1] [0,0,0,0] A draft node. Not used in the model. var a:= sum(scenarios_output,Zone); a:= a[Output_1='Vehicle km']; var b:= sum(sum(sum(a,vehicle),Length_1),Period_1); b:= (1-b/b[Comp_fr_1=0])*triangular(0,0.5,1); (1-b)*accidents*0.5 464,48,1 48,24 2,60,131,476,452 2,15,97,354,363,0,MIDM [Accidents,Comp_fr_1] [0,0,0,0] vehicles/h The average number of vehicles per hour driving along a link for the 30 most busy links at 8.00-9.00 in the morning. These numbers are for business-as-usual scenario where there is no composite traffic. var a:= Scen[Output_1='Link intensity',Length_1='< 5 km',.scenario=scenarios1_0_1.scenario]; var c:= Scenarios1_0_1[input_var='Composite fraction']; var g:= Scenarios1_0_1[input_var='Guarantee level']; a:= if c=0 and g=7 then a else 0; sum(sum(sum(a,a.scenario),Vehicle_noch),Zone) 64,256,1 48,16 2,403,80,476,527 2,10,318,563,321,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:5 Xmaximum:15 Yminimum:0 Ymaximum:1M Zminimum:1 Zmaximum:6 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1, 1, 1, 1, 1, 0, 0, 0] Probindex:[5%, 25%, 50%, 75%, 95%] Arial, 5 e/d Capital costs of the vehicle. It is assumed here that each vehicle is bought new and driven until the end of the vehicle's lifetime. In reality, of course many cars change owners during their lifetime, and this causes variation between individual car-owners about how much their way of owning a car really causes capital costs. However, this source of variation was excluded for simpilicity. This choice can be defended with an argument that those car-owners who spend most on the capital costs, i.e. buy the most expensive cars or sell them when they are still rather new, are likely to count a smaller fraction of the capital cost of the car when comparing different modes of transport. cars_needed*vehicle_price/vehicle_lifetime/365 176,32,1 48,16 [Period_1,Vehicle] e/d Time costs has two parts: the cost of delays due to traffic jams; and the cost of waiting for composite vehicles. The traffic jam cost includes only the direct costs of actual delays. However, a likely much bigger cost is the need to reserve extra time because of the risk of a traffic jam. If this was included, the costs for both car and composite passengers would be smaller especially with high volumes of composite traffic. index i:= ['Passengers in traffic jam','Waiting a composite vehicle']; var a:= sum(scenarios_output,Zone); var b:= a[Output_1='Waiting']/60*a[Output_1='Trips']; b:= if Vehicle='Car' then 0 else b*time_unit_cost; var c:= a[Output_1='Link intensity',Length_1='< 5 km']; c:= sum(c,Vehicle)/rush_bau; c:= 1-min([(1-c)/rush_delay[rush_delay='Reduction'],1]); var d:= a[Output_1='Trips']; d:= if periods=1 then d else 0; d:= d*rush_delay[rush_delay='Delay']*c*time_unit_cost; d:= array(i,[d,b]); sum(d,d.i) 176,256,1 48,16 2,30,65,476,526 2,165,294,634,303,0,MIDM [Comp_fr_1,Vehicle] [] e/d Salary and social security costs of the composite vehicle drivers. We assume that the drivers are paid only when driving, not when waiting for passengers. Although this might slightly underestimate the costs, this is a common practice among hired taxi drivers, who don't own the vehicle. var a:= sum(scenarios_output,Zone); a:= a[Output_1='Vehicle km']; a*driver_salary/traffic_speed 176,64,1 48,16 2,12,43,433,355,0,MIDM [Period_1,Vehicle] [Index Vehicle] e/d Costs due to fuel and maintenance. var a:= sum(scenarios_output,Zone); a:= a[Output_1='Vehicle km']; a*(fuel_price*fuel_consumption+car_maintenance) 176,96,1 48,16 [Period_1,Vehicle] e/d Fine particles are assumed to cause 10 deaths per 17 ton emission, a result from buses in Helsinki (1). CO2 costs are based on the estimated costs of CO2 in the greenhouse gas emission market. The true health and environmental costs are probably clearly higher than the price of the emission market. var a:= emission1*Pm_unit_lethality; a[emission='PM'] 176,224,1 48,16 2,431,209,476,224 2,301,50,639,305,0,MIDM [Vehicle,Comp_fr_1] e/d This version calculates emission costs per drive for a 10-km drive. Fine particles are assumed to cause 10 deaths per 17 ton emission, a result from buses in Helsinki (1). CO2 costs are based on the estimated costs of CO2 in the greenhouse gas emission market. The true health and environmental costs are probably clearly higher than the price of the emission market. var a:= 10*Emission_factor/1000*Emission_unit_cost; sum(a,emission) 320,176,1 48,16 2,301,50,262,305,0,MIDM [Vehicle,Emission] e/drive This version calculates the capital costs per trip assuming that each car takes 15 drives per day. Capital costs of the vehicle. It is assumed here that each vehicle is bought new and driven until the end of the vehicle's lifetime. In reality, of course many cars change owners during their lifetime, and this causes variation between individual car-owners about how much their way of owning a car really causes capital costs. However, this source of variation was excluded for simpilicity. This choice can be defended with an argument that those car-owners who spend most on the capital costs, i.e. buy the most expensive cars or sell them when they are still rather new, are likely to count a smaller fraction of the capital cost of the car when comparing different modes of transport. vehicle_price/vehicle_lifetime/365/15 320,112,1 48,16 2,340,200,476,312 2,82,146,654,409,0,MIDM [Vehicle,Comp_fr_1] e/drive Costs due to fuel and maintenance for a 10-km drive. 10*(fuel_price*fuel_consumption+car_maintenance) 320,144,1 48,16 [Period_1,Vehicle] e/trip Cost per trip of vehicle-dependent costs (=vehicle price, driving, emissions). The numbers are compared with the largest vehicle type. var a:= array(Cost_structure_1,[Vehicle_cost1,0,Driving_cost1,0,0,Emission_cost1,0,0,0]); a:= sum(a,Cost_structure_1)/4; a/a[vehicle='Bus no change'] 320,224,1 48,24 2,674,6,336,558,0,MEAN [Index Cost_structure_1] [0,0,0,0] There are three different stakeholders: Passenger, society, and bus company (which does not show up in the stakeholder index). See Stakeholder for more details. cost_to_stakeholder 392,432,1 52,36 1,1,1,1,1,1,0,,1, 2,245,4,476,458 The stakeholder-specific weights that are given to different cost types. The weight is 1 always with the following exceptions: - Car capital costs may be <1 because the owner may need the car for other purposes than the trips considered here. - Willingness to drive (Driver costs for car drivers) may be positive or negative depending on how the the driving is valuated. - 'Parking' is zero for composite traffic and society, because 'Parking land' cost is then calculated. - 'Parking land ' is zero for car passengers, because 'Parking' is then calculated. - 'Emission costs' and 'Accidents' are not calculated for passengers because they harm people in general, not any individual specifically. - 'Ticket' cost is calculated only for composite traffic passengers. It is not relevant for cars; and from the societal point of view, it is only a money transfer from the passenger to the service provider. Table(Cost_structure_1,Mode1,Stakeholder_1)( Cap,1, 1,1, (-Drive),(-Drive), 1,1, 1,1, 1,1, 1,1, 0,0, 0,0, 1,1, 0,1, 0,1, 1,1, 1,1, 0,1, 0,1, 0,0, 1,0 ) 176,360,1 48,24 2,102,90,476,355 2,61,68,416,303,0,MIDM 2,211,203,671,281,0,CONF [Stakeholder_1,Cost_structure_1] [Undefined,Mode1,1] [0,0,0,0] We were careful not to unrealistically exaggerate the benefits of the composite traffic. On the contrary, we excluded several clear but not easily quantifiable benefits: Reduced road traffic volumes save road management and infrastructure. City planning gets more freedom when the vehicle volumes decrease. This also improves the recreational values of the area. There may be an increase in walking and cycling, if the dependence on car is relieved. This may have positive secondary health effects in the population. transport_cost 480,216,1 68,52 1,1,1,1,1,1,0,,1, [Alias Costs_not_included_1] 1 176,496,1 68,72 1,1,1,1,1,1,0,,1, The_costs_are_calcul Replacement of quiet bus routes with composite traffic would probably improve service and reduce costs at the same time. Composite traffic is probably an efficient feeder for high-volume transport modes such as buses and metro. We assumed that the trips are uncorrelated in time (given the total volume at each time point). However, in reality a large proportion of trips is clustered: they are directed to or from particular places such as schools, offices, ballparks, and supermarkets at specific times. With composite vehicles, it results in more efficient trip aggregation; with cars, it results in local traffic jams. transport_cost 352,80,1 80,52 2,72,347,476,224 [0,0.2,0.4,0.6,0.8,1] 176,112,1 48,12 ['Yes','No'] 176,136,0 48,12 1,1,1,1,1,1,0,0,0,0 [0,0.2,0.4,0.6,0.8,1] 176,160,1 48,12 Value of information analyses, studies on variation in the population, and other analyses on the results. jtuomist Tue, Mar 27, 2001 11:26 jtue 12. Aprta 2005 16:35 48,24 544,232,0 48,29 1,1,1,1,1,1,0,0,0,0 1,87,134,695,523,17 94,1,1,0,2,9,4744,6798,7 trips/h Fig 1 in the main text. Trips by vehicle type as a function of time when the fraction of composite trips is 50% of the current personal car trips. In this graph, you can also view other composite fractions than 0.5 when guar is set to 7, and other other levels of guarantee when composite fraction is set to 0.5. var a:= Trips1_0; var b:= Scenarios1_0_1; a:= if b[input_var='Composite fraction']=Comp_fr_1 then a else 0; a:= if b[input_var='Guarantee level']=guar then a else 0; a:= if Comp_fr_1=0 then a[guar=7] else a; a:= a[guar=choose_guar]; a:= a[Comp_fr_1=choose_comp]; a:= if b[input_var='Flexible fraction']=choose_flexible then a else 0; a:= if b[input_var='No-change fraction']=choose_nochange then a else 0; a:= if b[input_var='Large guarantee?']='Yes' then (if large='Yes' then a else 0) else (if large='No' then a else 0); a:= a[large=choose_large]; a:= sum(a,Scenario1_0_1); a*array(vehicle,[1,0.5,1,0.5,0.5,1]) 544,96,1 48,24 2,120,33,476,590 2,161,13,835,589,1,MIDM [Formnode Figure_3] Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:5 Xmaximum:15 Yminimum:0 Ymaximum:1M Zminimum:1 Zmaximum:6 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1, 1, 1, 1, 1, 0, 0, 0] Probindex:[5%, 25%, 50%, 75%, 95%] Arial, 5 [Time_stat,Vehicle] Endpoints or pressures estimated. ['Fraction of composite trips without change (%)','Vehicles needed (number)','Parking places needed (number)','Average vehicle flow on the 30 most busy roads (vehicles/h at 8.00-9.00 AM)','Injuries due to accidents (cases per year)','Deaths due to accidents (cases per year)','Deaths due to fine particles (cases per year)','Fine particle (<2.5 µm of diameter) emissions (kg per day)','Carbon dioxide emissions (ton per day)','Driver salaries (thousand e per day)','Vehicle capital and operational costs (thousand e per day)','Time cost (thousand e per day)','Average car trip cost to passenger (e per trip)','Average composite trip cost to passenger (e per trip)'] 664,128,1 48,12 2,17,81,625,372 2,-3,30,512,303,0,MIDM Table 1 in the Main text of the article. To retrieve the same table, 'Choose guar' should be set to 7, 'Choose comp' to All, and 'Choose period' to All. Footnotes: Mean (90% confidence interval when applicable). If a passenger requests a trip without a transfer, the additional price to him/her will be 3 - 6 euro/trip during daytime. This cost is due to reduced efficiency in trip aggregation. The number of vehicles and parking places is theoretical and involves the modelled trips only; a car owner may need the car for trips outside Helsinki even if he/she uses composite traffic. The true number of cars in the area was 346 400 in 2001. (1) The current ticket prices for buses, metro, and trams are 1.70 e per trip in Helsinki and 2.90 e per trip between communities in the Helsinki metropolitan area. Note that the car trip and composite trip costs include time costs. var d:= sum(sum(sum(scenarios_output,Zone),Period_1),Length_1); d:= d[Comp_fr_1=i]; var a:= d[Output_1='Trips by vehicle']; a:= (slice(a,vehicle,1)+slice(a,vehicle,3)) +sum(sum(sum(no_change_trips[Comp_fr_1=i],Period_1),Length_1),Zone); a:= a/d[Output_1='Trips',vehicle='Bus no change']*100; var b:= sum(d[Output_1='Vehicles'],vehicle); var c:= sum(d[Output_1='Parking lot'],vehicle); d:= sum(d[Output_1='Link intensity'],vehicle); a:= rounding(a,3); b:= rounding(b,3); c:= rounding(c,3); d:= rounding(d,3); var e:= tm(sample(acc_num[accidents='Injuries'])); var f:= tm(sample(acc_num[accidents='Deaths'])); var g:= tm(sample(pm_lethality)*365); var h:= tm(sample(emission1[emission='PM'])); var i:= tm(sample(emission1[emission='CO2'])/1000); var j:= tm((if Vehicle='Car' then 0 else sample(driver_cost))/1k); var k:= tm(sample(vehicle_cost[guar=7])/1k+sample(driving_cost)/1k); var l:= tm(sample(time_cost)/1k); var x:= tm(sample(Cost_passenger)); var m:= (x[Mode1='Car']); var n:= (x[Mode1='Composite']); array(endpoint,[a,b,c,d,e,f,g,h,i,j,k,l,m,n]) 664,96,1 48,24 2,439,7,545,621 2,357,353,682,303,0,MIDM 2,5,2,990,352,0,MIDM [Formnode Table_4] Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:1 Zminimum:0 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] [I,Endpoint] 1,D,4,2,0,0 85,1,1,0,2,9,4744,6798,7 YTV: Liikkumisen nykytila (The Present-day Traffic Situation) PJS B 2001:10 <a hfref="http://www.ytv.fi/liikenne/julk/nykytila.pdf">PDF file</a> A list of uncertain variables used in the model. This list is used to analyse the role of each variable by e.g. value-of-information analysis or importance analysis. The variables with 'V:' are not uncertain but describe variability within the population. Note that the last variable 'Blank' is NOT included in the model and therefore whatever significance is attached to this variable, is just a random effect. Table(Uncertain_var)( Vehicle_price[Vehicle='Car'],Vehicle_lifetime[Vehicle='Car'],Fuel_price[Vehicle='Car'],Car_maintenance,Driver_salary,Rush_delay[Rush_delay='Delay'],Time_unit_cost,Trips_per_car,Emission_factor[Vehicle='Car', Emission='PM'],Emission_unit_cost[Emission='PM'],Sum(Sum(Sum(Accident_costs,Period_1),Vehicle),Length_1),Cap_uncert,Drive_uncert,Group_subvention,Group_size,Cap_variation,Drive_variation,Uniform(0,1)) ['Pollutant levels in fish feed after lower limits (S+P)','Salmon consumption after feed limits (S+P)','Does omega-3 help CHD patients or everyone? (S)','Dose-response of health benefit (S)','Highest omega-3 dose with health benefit (S)','Current average consumption of salmon (S)','Fraction of farmed from total salmon use (S)','Omega3 content in salmon (S)','Consider pollutant or net health effect? (P)','Dieldrin concentration in farmed salmon (S)','Toxaphene concentration in farmed salmon (S)','PCB concentration in farmed salmon (S)','Farmed salmon use after recommendation (S)','Lower limits for pollutants in fish feed? (P)','Recommend restricted farmed salmon consumption? (P)'] 408,56,1 48,24 1,1,1,1,1,1,0,0,0,0 2,541,193,476,275 2,335,42,655,455,0,MIDM 2,148,242,582,361,0,MIDM 52425,39321,65535 [Self,Self] A list of uncertain variables used in the model. ['Car price','Car lifetime','Fuel price','Vehicle maintenance','Driver salary','Delay due to rush','Unit cost of time','Trips per car','Car fine particle emission','Fine particle unit cost','Accident costs','Car capital','Willingness to drive','Group subvention','V: Car occupancy','V: Car capital','V: Willingness to drive','Blank'] 408,88,1 48,12 1,1,1,1,1,1,0,0,0,0 2,123,124,476,469 2,351,356,688,342,0,MIDM 2,168,178,582,361,0,MIDM [Self,Self] ['Car price','Car lifetime','Fuel price','Vehicle maintenance','Driver salary','Delay due to rush','Unit cost of time','Trips per car','Car fine particle emission','Fine particle unit cost','Accident costs','Car capital','Willingness to drive','Group subvention','V: Car occupancy','V: Car capital','V: Willingness to drive','Blank'] e/d Direct costs occurring to the society if it subsidises the composite traffic ticket prices so much that the target level of composite fraction is reached, i.e. that that fraction of population thinks that composite traffic is equally or more economic for them than car traffic. var a:= Expected_total_varia[Stakeholder_1='Passenger']; a:= Linearinterp(a.i,a, choose_comp,a.i); (a+mean(group_subvention))*trips_per_period[Period_1=choose_period, Mode1='Composite'] 288,208,1 48,24 2,102,90,476,475 2,336,56,550,289,1,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:1 Xmaximum:14 Yminimum:-100K Ymaximum:909.4K Zminimum:1 Zmaximum:2 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] Arial, 8 [Flexible_fr,Length_1] [Index Length_1] [0,0,0,0] e/trip This node is a combination of variables that represent variation, not uncertainty. In other words, all variation between the Monte Carlo iterations are due to variation within the population. (However, there are actually two variables, namely Cap_uncert and Drive_uncert that represent uncertainty of capital cost of car and willingness to drive, respectively. It would be tricky to separate these from variation, and therefore this discrepancy is allowed.) var a:= mean(Group_size)/sample(Group_size); a:= if Cost_structure_1 <>'Time' and Mode1='Car' then a else 1; a:= a*mid(cost_per_trip); a:= if isnan(a) then 0 else a; a:= a*cost_strength_variability; a:= sum(a,Cost_structure_1); {a:= a[stakeholder='Passenger',length='>= 5 km']; a[Mode1='Composite']-a[Mode1='Car']} 168,136,1 48,24 2,424,37,476,584 2,0,8,394,483,0,SAMP [Mode1,Run] e/trip Cost difference of composite and car trips shown as the expectation. The X axis shows the fractiles of the total variation within the population. See also 'Expected variations'. These lines are used in Figure 2 of the main text. See 'Figure 2'. var a:= cost_variation[Mode1='Composite']-cost_variation[Mode1='Car']; a:= variation1(a,Cost,9); var b:= a[.varia=1/9]+(a[.varia=1/9]-a[.varia=2/9])/2; var c:= a[.varia=9/9]+(a[.varia=9/9]-a[.varia=8/9])/2; index i:= [0,1/18,3/18,5/18,7/18,9/18,11/18,13/18,15/18,17/18,1]; array(i,[b,a[.varia=1/9],a[.varia=2/9],a[.varia=3/9],a[.varia=4/9],a[.varia=5/9],a[.varia=6/9],a[.varia=7/9],a[.varia=8/9],a[.varia=9/9],c]) 288,136,1 48,24 2,102,90,577,370 2,94,158,860,436,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:-4 Ymaximum:4 Zminimum:0.1111 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] [0,0,0,0] The number of classes in the value-of-information analysis. This is a technical parametre only, and it should be large enough. However, the samplesize should be at least 100 times larger than this to avoid random noise. 17 528,280,1 48,12 2,102,90,476,405 52425,39321,65535 fractile Total variation expressed as fractiles. See 'Cost variation'. var a:= sample(cost_variation); a:= a[Mode1='Composite']-a[Mode1='Car']; a:= rank(a,run)/samplesize; slice(a[guar=7,Comp_fr_1=0.5, Stakeholder_1='Passenger'], Period_1,1) 168,88,1 48,12 2,102,90,476,335 2,142,191,670,314,1,SAMP [Run,Length_1] 1,D,4,2,0,0 e/trip Value of information analysis for the input variables with the passenger decision between composite and car traffic. The analysis calculates the expected benefit for the passenger when the uncertainty of a variable is resolved. var a:= sample(cost__variation[Stakeholder_1='Passenger']); a:= sum(a*sum(trip_fraction,mode1),Length_1); Voi(a,Mode1,uncertain_inputs,uncertain_var,Classes) 408,208,1 48,24 2,68,266,476,483 2,28,44,735,480,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:1 Xmaximum:20 Yminimum:-0.3 Ymaximum:0 Zminimum:1 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] 1,F,4,3,0,0 [Index Comp_fr_1] e/d Total societal costs including subsidies. var a:= cost_to_stakeholder[Stakeholder_1='Society']; a:= a*trips_per_period[Period_1=choose_period]; if mode1='Composite' then a+subvention else a 288,280,1 48,24 2,102,90,476,270 2,104,11,736,486,0,MEAN [Comp_fr_1,Mode1] [Index Length_1] [0,0,0,0] e/d Value of information analysis for the input variables with the societal decision about the target level of composite fraction. Each level involves a particular amount of subsidies to composite traffic to reach the target. The analysis calculates the expected benefit for the society when the uncertainty of a variable is resolved. var a:= sum(sum(sample(Societal_cost__varia),Length_1),mode1); a:= if Comp_fr_1=1 then a[Comp_fr_1=0.9] else a; Voi(a,Comp_fr_1,uncertain_inputs,uncertain_var,classes) 408,408,1 48,24 2,506,97,476,310 2,18,41,377,506,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:9 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:1 Xmaximum:3 Yminimum:-70K Ymaximum:0 Zminimum:1 Zmaximum:12 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] Arial, 6 e/d Figure 3 top panel of the main text. Marginal societal costs of traffic (composite+car) as a function of the fraction that composite replaces personal car trips (composite fraction). Top: Societal costs (excluding subsidies for composite traffic) during different periods of day. To reproduce the figure in the article, set Choose comp = All Choose guar = 7 Choose period = All Subsidise groups? = No Choose large = No Choose_nochange = 0 Uncertainty options: Sample size 5000, random seed = 98, Median Latin Hypercube Warning: This will require > 1 MB of system memory var a:= sum(sum(Societal_cost,mode1)-subvention,Length_1); a-a[Comp_fr_1=0] 288,352,1 48,29 2,521,109,476,271 2,166,445,1069,316,0,MEAN [Formnode Figure_3_top2] [Comp_fr_1,Period_1] [0,0,0,0] e/d Figure 3 middle panel of the main text. Marginal societal costs of traffic (composite+car) as a function of the fraction that composite replaces personal car trips (composite fraction). Middle: Subsidies to ticket prices needed to reach the target fraction of composite traffic (i.e., to make that fraction of current car passengers to favour composite traffic). For comparison, the current subsidies to public transportation in Helsinki area are on the range of 380 000 e per day. The public transport subsidies in Helsinki, Espoo (incl Kauniainen), and Vantaa were 93.30, 25.95, and 19.49 million euro in 2003, which is approximately 380 000 euro per day for the whole area. To reproduce the figure in the article, set Choose comp = All Choose guar = 7 Choose period = All Subsidise groups? = No Choose large = No Choose_nochange = 0 Uncertainty options: Sample size 5000, random seed = 98, Median Latin Hypercube Warning: This will require > 1 MB of system memory var a:= sum(subvention,Length_1); a 168,208,1 48,24 2,120,77,476,224 2,62,10,968,199,0,MIDM [Formnode Figure_3_middle2] Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:-100K Ymaximum:100K Zminimum:1 Zmaximum:7 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] Arial, 6 [Comp_fr_1,Period_1] [Index Period_1] [Rosenberg, 2005 55 /id] <a href="http://www.mintc.fi/oliver/upl471-Julkaisuja_2_2005.pdf">PDF file</a> e/d Figure 3 bottom panel of the main text. Marginal societal costs of traffic (composite+car) as a function of the fraction that composite replaces personal car trips (composite fraction). Bottom: Societal costs (including subsidies) during daytime with increasing areal coverage of composite traffic (starting from the most densely populated areas). Both origin and destination must be in the covered area. The legend shows the number of inhabitants living in the covered area. (To see the legend, calculate Population_guaranteed.) To reproduce the figure in the article, set Choose comp = All Choose guar = All Choose period = 6.00-20.00 Subsidise groups? = No Choose large = No Choose_nochange = 0 Uncertainty options: Sample size 5000, random seed = 98, Median Latin Hypercube Warning: This will require > 1 MB of system memory var a:= Societal_cost; a:= a-a[Comp_fr_1=0]; sum(sum(a,Length_1),mode1); 168,352,1 48,24 2,411,30,476,357 2,61,120,1027,438,0,MEAN [Formnode Figure_3_bottom2] Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:-1.2M Ymaximum:200K Zminimum:1 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] Arial, 2 [Comp_fr_1,Period_1] [1,0,0,0] e/trip Figure 2 in the main text. Individual variation in the cost of a composite trip compared with a personal car trip for an individual passenger. The estimates include daytime trips with 50% composite fraction scenario. The trips are divided into two groups based on length. The variation between individuals is shown on X axis, with people most in favour of composite traffic on left. The expected values across individuals are shown as lines, and the dots represent the uncertainty of the value. Note that the lines of expectations are shown in another node, 'Expected total variation'. To reproduce the figure in the article, set Choose comp = 0.5 Choose guar = 7 Choose period = 6.00-20.00 Subsidise groups? = No Choose large = No Choose_nochange = 0 Uncertainty options: Sample size 1000, random seed = 98, Median Latin Hypercube slice(Cost[guar=7,Comp_fr_1=0.5,Stakeholder_1='Passenger'],Period_1,1) 168,56,1 48,24 2,102,90,476,385 2,159,34,670,538,1,SAMP [Formnode Figure_6] Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:4 Baroverlap:0 Linestyle:4 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:-4 Ymaximum:3 Zminimum:1 Zmaximum:2 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] Arial, 2 [Run,Length_1] Variation [0,0,0,0] e/trip The cost difference of the composite and car trips for the passenger (negative values: composite traffic is more beneficial). var a:= sample(cost_to_stakeholder{[stakeholder='Passenger']}); a:= a[Mode1='Composite']-a[Mode1='Car']; a 288,56,1 48,24 2,348,168,476,325 2,8,10,316,422,0,MEAN Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:-4 Ymaximum:10 Zminimum:1 Zmaximum:7 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1, 1, 1, 1, 1, 0, 0, 0] Probindex:[5%, 25%, 50%, 75%, 95%] Arial, 5 [Length_1,Comp_fr_1] [0,0,0,0] e/trip Same as 'Passenger VOI' except that the value of information is estimated for the subgroup that travels alone. var a:= sample(cost__variation[Stakeholder_1='Passenger']); a:= sum(a*sum(trip_fraction,mode1),Length_1); a:= if sample(Group_size)=1 then a else 0; Voi(a,Mode1,uncertain_inputs,uncertain_var,Classes) 408,280,1 52,24 2,15,127,476,224 2,393,95,352,473,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:1 Zminimum:0 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] 1,F,4,3,0,0 [Index Comp_fr_1] The highest VOI is in willingness to drive, when 'Car occupancy' is standardised to 1; otherwise the variation of 'Car occupancy' drives the VOI analysis. single_passenger_voi 664,240,1 48,38 65535,65532,19661 Composite traffic is more attractive to those with long (>= 5 km trips). Fig_4_cost_variation 56,56,1 52,48 [Alias Composite_traffic_i1] 65535,65532,19661 cost_to_stakeholder-(Cost_variation-mean(cost_variation)) 408,136,1 48,24 2,122,153,476,567 2,257,61,680,471,1,SAMP Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:4 Baroverlap:0 Linestyle:4 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1000 Yminimum:-3 Ymaximum:3 Zminimum:1 Zmaximum:2 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1, 1, 1, 1, 1, 0, 0, 0] Probindex:[5%, 25%, 50%, 75%, 95%] Arial, 8 [Run,Length_1] Variation e/d Total societal costs including subsidies. Here we exclude the variation so that the VOI is calculated based on uncertainty only. var a:= Cost_variation-mean(cost_variation); a:= cost_to_stakeholder-a; a:= a[Stakeholder_1='Society']; a:= a*trips_per_period[Period_1=choose_period]; if mode1='Composite' then a+subvention else a 408,344,1 48,24 2,542,125,476,224 2,154,69,736,486,1,MEAN Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:1 Xmaximum:14 Yminimum:0 Ymaximum:600K Zminimum:1 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] Arial, 8 [Comp_fr_1,Undefined] [Index Length_1] [0,0,0,0] ktluser 10. touta 2005 21:28 48,24 664,32,1 48,24 1,0,0,1,1,1,0,,0, 1,21,134,510,528,17 trips/d Number of trips per day by vehicle type. Set guar to 7 to view the trips as a function of composite fraction. Set comp fr to 0.5 to view the trips as a function of guarantee level. sum(Fig_2_trips,time_stat)*time_unit 312,400,1 48,24 2,102,90,476,345 2,13,28,811,629,1,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:1 Xmaximum:7 Yminimum:0 Ymaximum:100K Zminimum:1 Zmaximum:6 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] Arial, 5 var a:= slice(time_stat,time_stat,ceil(rank(time_stat)/2)*2-1); index tim:= sequence(0,max(time_stat),time_unit*2); sum((if tim=a then Fig_2_trips else 0),time_stat)/2 312,344,1 48,24 2,320,289,476,224 2,7,15,469,587,1,MIDM e/trip Costs per trip to the passenger. var b:= cost__variation; var a:= (sum(sum(trips_per_period,Length_1),Period_1)); a:= trips_per_period/a; a:= sum(sum(a*b,Length_1),Period_1); a[Stakeholder_1='Passenger'] 176,360,1 48,24 2,641,24,476,562 2,132,15,788,516,1,MEAN [Comp_fr_1,Mode1] [Index Cost_structure_1] [0,0,0,0] e/trip The cost per trip for a random individual passenger. These values have been weighted by the stakeholder-specific weights (Cost strength). The costs are first calculated for an average trip from total costs and total numbers of trips. The costs of individual car trips depend on the number of passengers. Therefore, the average cost is multiplied by the average number of passengers and divided by the number of passengers in the particular case we are looking at. var a:= mean(Group_size)/sample(Group_size); a:= if Cost_structure_1 <>'Time' and Mode1='Car' then a else 1; a:= a*cost_per_trip[Comp_fr_1=0.5,guar=7]; a:= if isnan(a) then 0 else a; a:= a*cost_strength; var b:= trips_per_period[Comp_fr_1=0.5, guar=7]; b:= b/(sum(sum(b,Length_1),Period_1)); sum(sum(a*b,Length_1),Period_1); 176,480,1 52,24 2,589,137,476,456 2,13,10,784,205,0,MIDM [Formnode Cost_by_type_to_sta1] Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:9 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:10 Yminimum:0 Ymaximum:0.6 Zminimum:1 Zmaximum:2 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.95] Arial, 2 [Cost_structure_1,Stakeholder_1] [0,0,0,0] [Mode1,1,Stakeholder_1,1,Cost_structure_1,1] var a:= trips_per_period/sum(sum(trips_per_period,Length_1),mode1); a[Period_1=choose_period] 176,208,1 48,24 2,18,307,416,303,0,MIDM [Mode1,Length_1] [Index Length_1] # or #/h A set of scenarios organised along two indexes: Guar is the level of composite traffic guarantee. This means that trips within a certain area will be organised by composite travel, while areas outside this guarantee remain without the service. The point in using this index is to explore whether composite traffic can be started with low profile and expanded geographically as more people start using it. Comp_fr is the fraction of trips that were previously performed by personal car but are performed by composite traffic in the scenario. var a:= Scen; a:= a[vehicle_noch='No-change',Output_1='Trips by vehicle']; var b:= Scenarios1_0_1[.scenario=a.scenario]; a:= if b[input_var='Flexible fraction']=flexible_fr then a else 0; a:= a[flexible_fr=choose_flexible]; a:= if b[input_var='No-change fraction']=nochange_fr then a else 0; a:= a[nochange_fr=choose_nochange]; a:= if b[input_var='Large guarantee?']='Yes' then (if large='Yes' then a else 0) else (if large='No' then a else 0); a:= a[large=choose_large]; a:= if b[input_var='Composite fraction']=Comp_fr_1 then a else 0; a:= if b[input_var='Guarantee level']=guar then a else 0; a:= sum(a,a.scenario); a:= if Comp_fr_1=0 then a[guar=7] else a; a:= a[Comp_fr_1=choose_comp]; a:= a[guar=choose_guar]; a[Period_1=choose_period] 56,424,1 48,24 2,140,23,476,548 2,69,383,591,222,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:5 Xmaximum:15 Yminimum:0 Ymaximum:1M Zminimum:1 Zmaximum:6 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1, 1, 1, 1, 1, 0, 0, 0] Probindex:[5%, 25%, 50%, 75%, 95%] Arial, 5 [Length_1,Zone] e/trip Calculates the additional cost to those passengers that want a direct trip even if there is not a full vehicle available. First, the additional cost per trip of having these trips in the system is calculated. This is multiplied by the total number of trips to get the total additional cost per day. This is divided by the number of these special-service trips. Taken together, everyone must pay the price shown with No-change fraction=0, and the No-change cost is added to this price to cover the additional costs. var a:= cost_passenger-cost_passenger[nochange_fr=0]; a:= a[mode1='Composite']; a:= a*sum(trips_per_period[Period_1=choose_period,mode1='Composite'],Length_1); var b:= sum(sum(no_change_trips,Length_1),Zone); a/b 176,424,1 48,24 2,102,90,485,430 2,281,258,643,324,0,MEAN [Comp_fr_1,Period_1] [Index Cost_structure_1] The same as Cost strength, except that this node only contains the variability, not uncertainty. The stakeholder-specific weights that are given to different cost types. The weight is 1 always with the following exceptions: - Car capital costs may be <1 because the owner may need the car for other purposes than the trips considered here. - Willingness to drive (Driver costs for car drivers) may be positive or negative depending on how the the driving is valuated. - 'Parking' is zero for composite traffic and society, because 'Parking land' cost is then calculated. - 'Parking land ' is zero for car passengers, because 'Parking' is then calculated. - 'Emission costs' and 'Accidents' are not calculated for passengers because they harm people in general, not any individual specifically. - 'Ticket' cost is calculated only for composite traffic passengers. It is not relevant for cars; and from the societal point of view, it is only a money transfer from the passenger to the service provider. Table(Cost_structure_1,Mode1,Stakeholder_1)( Cap_variation,Cap_variation, 1,1, (-Drive_variation),(-Drive_variation), 1,1, 1,1, 1,1, 1,0, 0,0, 0,1, 1,1, 0,1, 0,1, 1,1, 1,1, 0,1, 0,1, 0,0, 1,0 ) 312,456,1 48,24 2,102,90,476,422 2,61,68,416,303,0,MIDM 2,564,215,350,281,0,MEAN [Mode1,Cost_structure_1] [Mode1,Cost_structure_1] Most of the VOI (esp. car occupancy) is actually in variables known to the passenger. passenger_voi_and_voc 456,208,1 48,63 65535,65532,19661 Most of the VOI is actually VOC=value of consensus. This means that VOI is calculated for an input variable that is not actually unknown, but it reflects true variability in the population. Therefore the reduction of the spread of this variable does not mean that uncertainty is decreased. It means that the variability is decreased, i.e. that the population is approaching consensus. Societal_voi_and_voc 336,128,1 48,46 65535,65532,19661 Spearman r Importance analysis of the uncertain input variables. It is a Spearman rank correlation between the input variables and the outcome ('Cost'). Abs( RankCorrel( Uncertain_inputs,Cost) ) 64,120,1 48,24 1,1,1,1,1,1,0,0,0,0 2,127,41,402,453,0,MIDM [Length_1,Uncertain_var] fractile Uncertain input variables standardised as fractiles. rank(uncertain_inputs,run)/samplesize 64,72,1 48,12 2,97,189,665,420,0,SAMP [Run,Uncertain_var] e/trip Cost difference of composite and car trips shown as the expectation. The X axis shows the fractiles of one uncertain variable. If there is a trend, this varible has a large impact on the cost difference. See also 'Cost by uncertainty'. variation1(uncertain_inputs,Cost,9) 64,240,1 48,24 2,116,222,476,224 2,12,12,607,474,1,MIDM [Comp_fr_1,Uncertain_var] e/trip An alternative way of calculating costs given a certain input variable ('Car occupancy' in this case). var classes:= 100; index varia:= 1..classes; var c:= getfract(Group_size,varia/classes); average(for x[]:= c do (whatif(Costs__cap,Group_size,x)),varia) 176,304,1 48,24 2,45,0,833,212,1,SAMP Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:100 Yminimum:-4 Ymaximum:4 Zminimum:1 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1, 1, 1, 1, 1, 0, 0, 0] Probindex:[5%, 25%, 50%, 75%, 95%] Arial, 6 e/trip An alternative way of calculating costs given a certain input variable ('Cap' in this case). var classes:= 100; index varia:= 1..classes; var c:= getfract(cap,varia/classes); average(for x[]:= c do (whatif(Cost,cap,x)),varia) 64,304,1 48,24 2,47,207,833,237,1,SAMP Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:100 Yminimum:-4 Ymaximum:4 Zminimum:1 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1, 1, 1, 1, 1, 0, 0, 0] Probindex:[5%, 25%, 50%, 75%, 95%] Arial, 6 e/trip Cost difference of composite and car trips shown as a scatter plot. The X axis shows the fractiles of one uncertain variable. If there is a trend, this varible has a large impact on the cost difference. Cost 64,40,1 48,24 2,82,65,830,529,1,SAMP Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:4 Baroverlap:0 Linestyle:4 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:-2.5 Ymaximum:2.5 Zminimum:1 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1, 1, 1, 1, 1, 0, 0, 0] Probindex:[5%, 25%, 50%, 75%, 95%] Arial, 6 [Run,Undefined] Uncertainties e/trip An alternative way of calculating costs given a certain input variable ('Car occupancy' in this case). var classes:= 100; index varia:= 1..classes; var c:= getfract(Group_size,varia/classes); average(for x[]:= c do (whatif(societal_cost,Group_size,x)),varia) 64,184,1 48,24 2,45,0,833,212,1,SAMP Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:100 Yminimum:-4 Ymaximum:4 Zminimum:1 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1, 1, 1, 1, 1, 0, 0, 0] Probindex:[5%, 25%, 50%, 75%, 95%] Arial, 6 var x:= 9; var a:= sample(cost_variation); a:= a[Stakeholder_1='Passenger']; a:= a[mode1='Composite']-a[mode1='Car']; index vari:= sequence(1/x,1,1/x); var in:= ceil(rank(a,run)*x/samplesize)/x; a:= if in=Vari then a else 0; 176,40,1 48,24 [Run,Comp_fr_1] The iterations are classified into 9 groups based on variability, and these groups are calculated separately. This is reasonable, because there is no point in calculating a common VOI for two individuals, who are on opposite extremes of the variation according to favourness of composite traffic. However, both Passenger VOI and Single passenger VOI are doing this (except that the latter matches for the most important variating variable). var a:= array(Mode1,[sample(Cost_classified)*9,0]); a:= sum(a*sum(trip_fraction,mode1),Length_1); Voi(a,Mode1,uncertain_inputs,uncertain_var,Classes) 336,40,1 52,24 2,389,72,366,497,0,MIDM e/d Value of information analysis for the input variables with the societal decision about the target level of composite fraction. Each level involves a particular amount of subsidies to composite traffic to reach the target. The analysis calculates the expected benefit for the society when the uncertainty of a variable is resolved. var a:= sum(sum(sample(Societal_cost),Length_1),mode1); a:= if Comp_fr_1=1 then a[Comp_fr_1=0.9] else a; Voi(a,Comp_fr_1,uncertain_inputs,uncertain_var,classes) 176,128,1 48,24 2,581,43,377,506,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:9 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:1 Xmaximum:17 Yminimum:-225K Ymaximum:0 Zminimum:1 Zmaximum:2 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] Arial, 6 e/trip Value of information analysis for the input variables with the passenger decision between composite and car traffic. The analysis calculates the expected benefit for the passenger when the uncertainty of a variable is resolved. var a:= sample(cost[Stakeholder_1='Passenger']); a:= sum(a*sum(trip_fraction,mode1),Length_1); a:= array(mode1,[a,0]); Voi(a,Mode1,uncertain_inputs,uncertain_var,Classes){missing ')'} 312,208,1 48,24 2,482,80,398,480,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:1 Zminimum:0 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] 1,F,4,3,0,0 var a:= cost_variation[Mode1='Composite']-cost_variation[Mode1='Car']; a:= rank(a,run)/samplesize; var b:= expected_total_varia; a:= Linearinterp(b.i,b, a,b.i); cost-a 424,344,1 48,24 2,102,90,476,375 2,257,61,680,471,1,SAMP Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:4 Baroverlap:0 Linestyle:4 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1000 Yminimum:-3 Ymaximum:3 Zminimum:1 Zmaximum:2 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1, 1, 1, 1, 1, 0, 0, 0] Probindex:[5%, 25%, 50%, 75%, 95%] Arial, 8 [Run,Length_1] For the societal question whether to subsidise composite traffic at 50 % composite fraction or not at all, the total value of resolving all uncertainty is only about 30 000 e per day, and the value for every single variable was zero. This means that the conclusion is robust and that even if the truth about a variable were found out to be the most unfavourable to the composite traffic, the optimal decision would still be the same. Societal_voi_0_or_50 648,408,1 52,44 2,102,90,475,224 [Alias Total_societal__voi1] 65535,65532,19661 passenger_voi 528,208,1 48,29 2,102,90,476,471 2,17,59,1118,451,0,MIDM [Formnode Fig_6a_passenger_vo1] Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:10 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:1 Xmaximum:14 Yminimum:-0.35 Ymaximum:0 Zminimum:1 Zmaximum:7 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] Alba, 8 [Comp_fr_1,Sys_localindex('VARIABLE')] [1,0,0,0] [Period_1,1,Sys_localindex('VARIABLE'),1,Comp_fr_1,1] Societal_voi_0_100 288,408,1 48,24 2,189,79,432,433,0,MIDM [Formnode Fig_6b_societal_voi1] [Period_1,Sys_localindex('VARIABLE')] e/d Value of information analysis for the input variables with the societal decision about the target level of composite fraction. Each level involves a particular amount of subsidies to composite traffic to reach the target. The analysis calculates the expected benefit for the society when the uncertainty of a variable is resolved. var a:= sum(sum(sample(Societal_cost__varia),Length_1),mode1); index comp:= [0,0.5]; a:= a[Comp_fr_1=comp]; Voi(a,comp,uncertain_inputs,uncertain_var,classes) 528,408,1 48,24 2,18,41,377,506,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:9 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:1 Xmaximum:3 Yminimum:-70K Ymaximum:0 Zminimum:1 Zmaximum:12 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] Arial, 6 (a:probtype) a:= if size(a)=size(sum(a,Length_1)) then a else sum(a,Length_1); a:= if size(a)=size(sum(a,vehicle)) then a else sum(a,vehicle); a:= if size(a)=size(sum(a,Period_1)) then a else sum(a,Period_1); a:= rounding(mean(a),3)&' ('& rounding(Getfract(a,0.05),3)&'-'& rounding(getfract(a,0.95),3)&')'; a:= if a='NAN (NAN-NAN)' then '' else a; a:= a; a[Comp_fr_1=i] 664,176,1 48,12 2,7,86,476,512 a [0,0.25,0.5,0.75,1] 664,152,1 48,12 This module calculates the trip rate for each origin-destination pair (129^2 pairs) and for each time point (12 min intervals resulting in 120 time points) based on trip data from three separate hours (morning rush, midday, afternoon rush) and time activity (based on diaries) in traffic along 24 hours. The total number of trips equals the number of car trips in Helsinki area on a working day in 2000. All scenarios have the same street strucure and number of trips with a particular origin, destination, and time. The trips are divided into car trips and composite trips differently in each scenario based on two variables. Composite fraction is the percentage of the trips that are handled by composite traffic; the remaining trips are handled by personal cars. Guaranteed area defines the area where composite traffic is provided (i.e. the area where you are guaranteed to get a composite vehicle if you want one). The default assumption is that both the origin AND the destination must be in the guaranteed area, but it is also easy to evaluate scenarios where the guarantee covers all trips in the Helsinki area as long as either the origin OR the destination is in the guaranteed area. The model calculates the expected number of trips for each origin-destination-time cell, and picks one random number from Poisson distributioin based on the expectation. After that, the model is deterministic all the way to Outputs node. jtue 26. Junta 2003 12:49 jtue 18. elota 2004 18:12 48,24 184,232,1 48,24 1,1,1,1,1,1,0,0,0,0 1,146,3,492,493,17 2,244,212,476,362 Arial, 13 trips/time unit Calculates number of individuals in the composite traffic and in car traffic for each route and time. Composite traffic may be restricted by area or by the fraction of trips that switch from car traffic to composite traffic. var a:= max([100u,adjusted_trip_rate]); a:= slice(sample(Poisson(a)),run,1); var g:= Scenario_input[input_var='Guarantee level']; var comp:= Scenario_input[input_var='Composite fraction']; var b:= guaranteed_areas; var c:= From&''; b:= if findintext(c,Regions) then b else 0; b:= sum(b,Region); b:= b[guarantee=g]; b:= if Scenario_input[input_var='Large guarantee?']='Yes' then b+b[From=To1] else b*b[From=To1]; b:= if b>0 then 1 else 0; b:= b*comp; b:= slice(sample(binomial(a,b)),run,1); array(Mode1,[a-b,b]) 336,160,1 48,24 2,461,110,476,493 2,43,0,831,468,0,MIDM [Time,From] [Index Mista] passengers/time unit Passenger flow at each point. This is a sum of people who start, continue or end their trip from or to here. var a:= From&''; var c:= sum(All_trips[Mode1='Composite'],time); for x[]:= a do ( var b:= (if findintext(x,Route_matrix)>0 then c else 0); sum(sum(b,From),To1) ) 448,160,1 48,24 2,102,90,476,316 2,142,149,654,249,0,MIDM [To1,From] The most busy point along the trip. In a case where there is no direct composite vehicle driving from the origin to the destination, the passenger is dropped at this point, and the latter part of the trip is organised separately. index etappi:= 1..max(max((textlength(route_matrix)+1)/5,From),To1); var a:= sum(Flow,To1); var b:= '0*'&Route_matrix&'*0'; b:= for x[]:= b do slice(splittext(x,','),etappi); var c:= a[From=evaluate(b)]; var d:= if istext(c) or isnumber(c) then c else 0; c:= argmax(d,etappi); c:= if max(d,etappi)=0 or c=1 then '' else b[etappi=c]&','; From&','&c&To1 448,224,1 48,24 2,504,112,476,513 2,43,10,972,486,0,MIDM [To1,From] A dummy index. [1,2,3,4,5,6,7] 336,256,1 48,12 2,102,90,476,533 2,104,114,416,494,0,MIDM [1,2,3,4,5,6,7] Guarantee means that any trip within the specified region is organised by the composite traffic, if wanted. 1=guarantee, 0=no guarantee. The default assumption is that both the origin AND the destination must be in the guaranteed area, but it is also easy to evaluate scenarios where the guarantee covers all trips in the Helsinki area as long as either the origin OR the destination is in the guaranteed area. Table(Guarantee,Region)( 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1, 1,1,1,1,1,1,1,1,0,1,1,1,1,1,1, 1,1,1,1,1,1,1,1,0,1,0,1,1,1,1, 1,1,1,1,1,1,1,1,0,0,0,1,1,1,1, 1,1,1,1,1,1,1,1,0,0,0,0,0,0,0, 1,1,0,0,1,1,1,1,0,0,0,0,0,0,0, 1,1,0,0,1,0,1,0,0,0,0,0,0,0,0 ) 336,224,1 48,24 2,102,90,476,434 2,354,113,626,449,0,MIDM 2,19,246,688,430,0,MIDM 52425,39321,65535 [Guarantee,Region] [Guarantee,Region] Input variable values for base case scenario. If Large guarantee? is 'Yes', then it is assumed that the guarantee covers the whole area, if the origin OR the destination of the trip are in the guaranteed area. Otherwise, both O and D must be in the covered area. Table(Input_var)( 0.5,7,0,0,0) ['Composite fraction','Guarantee level','Lim'] 336,80,1 48,24 2,597,125,416,303,0,MIDM 52425,39321,65535 [Self,Scenario] Index for variables that may affect the number of composite traffic trips. ['Composite fraction','Guarantee level','Large guarantee?','No-change fraction','Flexible fraction'] 336,112,1 48,12 ['Composite fraction','Guarantee level','Large guarantee?','No-change fraction','Flexible fraction'] trips/time unit Calculates the traffic volume for each time point of the day. First, the matrix is selected based on the Base_time Name column, and then the numbers are scaled as the proportion of the traffic activity per each hour and the peak hour for which the matrix was calculated. var c:= Trips_by_hour[Reg=From,Reg1=To1]; c:= if c=null then 0 else c; c:= cubicinterp(hour,c,time,hour) 168,96,1 48,24 2,454,141,476,358 2,151,124,782,471,0,MIDM [Time,From] [To1,From] trips/time unit Calculates the traffic volume for each time point of the day. Adjusting is taken into account to yield results where the population in an area is not much different after the day. var g:= unadjusted_trip_rate; {index x:= copyindex(From); var b:= 0; var c:= 0; var e:= 0; var a:= sum(Unadjusted_trip_rate,time); b:= sum(a,From); b:= b[To1=From]; c:= sum(a,To1); c:= (b-c)*a/sum(a,To1); e:= if c<0 then -c else 0; c:= if c<0 then 0 else c; e:= e[From=x,To1=From]; e:= e[x=To1]; a:= c+e; var g:= if time>7 and time<19 then 1 else 0; g:= g/sum(g,time); g:= Unadjusted_trip_rate+a*g;} g:= g/sum(sum(sum(g,from),to1),time)*total_trips; var h:= if rank(time)/2 = floor(rank(time)/2) then g *scenario_input[input_var='Flexible fraction'] else 0; var i:= h[time=time+time_unit] ; i:= if i=null then 0 else i; g+i-h 168,160,1 48,24 2,26,34,476,518 2,578,39,507,476,0,MIDM Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:1 Zminimum:0 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] [Time,From] [To1,From] trips Total number of trips travelled in a personal car in Helsinki Metropolitan area during a working day. The total number of trips is 2.9 million, and 44% of them are by personal cars. Trips by traffic mode on weekday in the Helsinki metropolitan area in 2000. Total trips 2.9 million 22 % Walking 7 % Cycling 16 % Bus 3 % Tram 3 % Train 4 % Metro 34 % Personal car (driver) 10 % Personal car (passenger) and taxi 2.9M*0.44 56,160,1 48,24 2,102,90,476,478 65535,52427,65534 YTV: Helsingin seudun nykytila (The Current State of Helsinki Region) PJS B 2002:1 <a hfref="http://www.ytv.fi/seutukeh/pks/pks2025/nykytila.pdf">PDF file</a> inhabitants Population of the Helsinki Metropolitan Area by area in 2003. Table(Area1)( 389,10.248K,8215,882,6768,4157,11.62K,761,2407,3401,13.137K,14.569K,8705,6832,4746,0,3542,2284,15.89K,7028,11.8K,6825,3344,5755,10.28K,19K,9940,7288,12.956K,12.983K,10.358K,4523,8375,12.656K,5284,8470,13.653K,6422,8695,3549,8782,4169,11.435K,10.766K,2122,5480,7962,11.615K,10.91K,7636,5795,3710,16.146K,9493,8819,8331,11.226K,4023,8631,28.283K,5951,8259,16.458K,13.495K,12,829,9,3235,9228,6191,3145,7835,8819,16.405K,14.91K,6105,8003,15.762K,14.608K,2209,2888,12.29K,7692,3475,8069,2237,5239,8905,9199,8253,15.238K,5847,5934,1845,4671,549,3999,572,3579,9299,6466,18.695K,14.052K,2140,4118,2619,112,3145,3465,215,47,1807,10.396K,4301,11.36K,4840,2895,1346,3723,8338,2620,5403,3375,9873,12.478K,3167,4698,14.244K,9899,0) 56,224,1 48,24 2,388,82,476,459 2,415,198,416,303,0,MIDM 2,510,11,258,615,0,MIDM 65535,52427,65534 Seutu-CD '03. YTV (The Helsinki Metropolitan Area Council), Helsinki, 2004. inhabitants Number of inhabitants in the area in which the composite traffic operates. var b:= guaranteed_areas; var c:= From&''; b:= if findintext(c,Regions) then b else 0; {b:= sum(b,Region);} b:= if b>0 then population[area1=from] else 0; sum(b,from) 168,224,1 48,24 2,480,131,476,440 2,202,71,609,369,0,MIDM [Guarantee,Region] [Index Region] arbitrary The areal surface of each area. (A rough classification). Table(Region)( 7,4,3,2.5,5,2,3,1,1,1,1,1,1,2,3) 56,288,1 48,24 2,541,153,416,352,0,MIDM 2,526,136,416,386,0,MIDM 65535,52427,65534 Based on rough estimates with a map on scale 1:40000. arbitrary Population density in each area. (A rough classification.) var c:= From&''; var b:= if findintext(c,Regions) then 1 else 0; b:= if b>0 then population[area1=from] else 0; sum(b,from)/areal_surface 168,288,1 48,24 2,481,162,476,400 2,93,219,954,423,0,MIDM [From,Region] jtue 13. Febta 2003 16:03 ktluser 25. touta 2005 12:30 48,24 168,32,1 48,24 1,1,1,1,1,1,0,0,0,0 1,85,42,48,530,17 Arial, 13 Hour of day. Sequence( 0, 23 ) 400,272,1 48,12 1,1,1,1,1,1,0,,0, 1,104,114,416,303,0,MIDM [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23] (param1, param2;suurind,pienind:indextype;indtieto) A function used to divide aggragate data into its disaggregate units based on weighting factors. using a:=sum((if indtieto=Suurind then param1 else 0), pienind) do using b:= sum((if indtieto=suurind then a else 0), suurind) do param2/b 168,368,1 48,24 2,591,58,476,514 param1,param2,suurind,pienind,indtieto (param1, param2; suurind, pienind:indextype;indtieto) A function used to divide aggragate data into its disaggregate units based on weighting factors. using a:= Normitus(param2,param2,suurind,pienind,indtieto) do using b:= (if indtieto=suurind then param1*a else 0) do using c:= sum(b, suurind) do c 168,424,1 48,24 2,36,83,476,312 param1,param2,suurind,pienind,indtieto 1000 tips/d One-way trips from one municipality to another. Table(Municipality,Municipality1)( 223,(365/2),(130/2),(95/2), (365/2),332,(103/2),(117/2), (130/2),(103/2),320,(49/2), (95/2),(117/2),(49/2),179 ) 56,64,1 48,24 2,422,91,476,513 1,77,139,758,383,0,MIDM 2,52,332,708,188,0,MIDM 65535,52427,65534 [Self,Municipality1] [Municipality,Municipality1] [Index Suuralue] YTV: Liikkumisen nykytila. PŠŠkaupunkiseudun julkaisusarja B 2001:10. Fig 6. <a href="http://www.ytv.fi/NR/rdonlyres/F6B8A4F8-C394-4972-A1DE-C64E2B69EE6D/0/nykytila_B2001_10.pdf>PDF file</a> 1000 trips/d One-way trips from one place to another (such as home, work etc). Table(Place,Place1)( 29,(642/2),(67/2),(283/2),(1315/2), (642/2),4,(71/2),(9/2),(184/2), (67/2),(71/2),21,(1/2),(21/2), (283/2),(9/2),(1/2),2,(50/2), (1315/2),(184/2),(21/2),(50/2),193 ) 56,176,1 48,24 2,402,104,476,603 2,44,37,504,196,0,MIDM 65535,52427,65534 [Place,Place1] [Place,Place1] [Index Kohde] YTV: Liikkumisen nykytila. PŠŠkaupunkiseudun julkaisusarja B 2001:10. Fig 7. <a href="http://www.ytv.fi/NR/rdonlyres/F6B8A4F8-C394-4972-A1DE-C64E2B69EE6D/0/nykytila_B2001_10.pdf>PDF file</a> fraction The distribution of trips among transportation modes. Table(Place,Place1,Mode2)( 0.34,0.19,0.46,0.01, 0.15,0.39,0.46,0, 0.34,0.19,0.46,0.01, 0.42,0.42,0.15,0.01, 0.34,0.19,0.46,0.01, 0.15,0.39,0.46,0, 0.25,0.24,0.5,0.01, 0.25,0.24,0.5,0.01, 0.25,0.24,0.5,0.01, 0.25,0.24,0.5,0.01, 0.34,0.19,0.46,0.01, 0.25,0.24,0.5,0.01, 0.25,0.24,0.5,0.01, 0.25,0.24,0.5,0.01, 0.25,0.24,0.5,0.01, 0.42,0.42,0.15,0.01, 0.25,0.24,0.5,0.01, 0.25,0.24,0.5,0.01, 0.25,0.24,0.5,0.01, 0.25,0.24,0.5,0.01, 0.34,0.19,0.46,0.01, 0.25,0.24,0.5,0.01, 0.25,0.24,0.5,0.01, 0.25,0.24,0.5,0.01, 0.25,0.24,0.5,0.01 ) 64,288,1 48,24 2,377,111,476,441 1,494,125,416,303,0,MIDM 2,27,185,456,199,0,MIDM 65535,52427,65534 [Mode2,Place1] [Place,Place1] [Index Kohde] YTV: Liikkumisen nykytila. PŠŠkaupunkiseudun julkaisusarja B 2001:10. Fig 8. <a href="http://www.ytv.fi/NR/rdonlyres/F6B8A4F8-C394-4972-A1DE-C64E2B69EE6D/0/nykytila_B2001_10.pdf>PDF file</a> trips/d/inh Number of trips per inhabitant of each transportation mode in different municipalities. These data are not used in the model. Table(Municipality,Mode2)( 1.31,1.1,0.93,0.03, 0.89,1.01,1.34,0.03, 0.92,0.72,2.03,0.03, 0.92,0.73,1.67,0.05 ) 64,368,1 48,24 2,491,162,476,551 1,136,146,595,314,0,MIDM 2,30,208,649,187,0,MIDM 65535,52427,65534 [Mode2,Self] [Municipality,Mode2] [Index Suuralue] YTV: Liikkumisen nykytila. PŠŠkaupunkiseudun julkaisusarja B 2001:10. Fig 9. <a href="http://www.ytv.fi/NR/rdonlyres/F6B8A4F8-C394-4972-A1DE-C64E2B69EE6D/0/nykytila_B2001_10.pdf>PDF file</a> fraction The fraction of public transportation in municipalities. These data are not used in the model. Table(Municipality,Municipality1)( 0.64,0.59,0.5,0.57, 0.59,0.33,0.24,0.21, 0.5,0.24,0.22,0.14, 0.57,0.21,0.14,0.23 ) 64,424,1 48,24 2,102,90,476,471 1,200,210,666,291,0,MIDM 1,200,210,752,301,1,MIDM 65535,52427,65534 [Self,Municipality1] [Self,Municipality1] YTV: Liikkumisen nykytila. PŠŠkaupunkiseudun julkaisusarja B 2001:10. Fig 6. <a href="http://www.ytv.fi/NR/rdonlyres/F6B8A4F8-C394-4972-A1DE-C64E2B69EE6D/0/nykytila_B2001_10.pdf>PDF file</a> A rough weighting of different trips along the day. The purpose of this node is to take into account the fact that residences and workplaces are located differently in the area, and therefore the different trips occur unevenly in time and space. var a:= table(Time_of_day)(0.1,0.3,1,0.1,0.1); var c:= table(Time_of_day)(1,0.3,0.2,0.1,0.1); a:= (if Place='Workplace' or Place='Business' then a else if Place1='Workplace' or Place1='Business' then c else 1); a:= a[Time_of_day=Time_of_day_by_hour]; a/sum(a,hour) 504,96,1 48,24 2,667,115,476,570 2,400,26,509,574,0,MIDM 52425,39321,65535 [Place1,Hour] [Index Tunti] Municipalities in the Helsinki metropolitan area. Helsinki is divided into two parts; Kauniainen is together with Espoo. ['Helsinki, downtown','Helsinki, suburbs','Espoo, Kauniainen','Vantaa'] 56,96,1 48,12 2,243,104,476,437 2,17,221,416,303,0,MIDM ['Helsinki, downtown','Helsinki, suburbs','Espoo, Kauniainen','Vantaa'] The same as Municipality; this index is used as the destination. copyindex(Municipality) 56,120,1 48,12 2,451,144,476,421 2,72,82,416,303,0,MIDM ['Helsinki, downtown','Helsinki, suburbs','Espoo, Kauniainen','Vantaa'] The place where the trip origines/ends. Workplace is a trip to/from the workplace; business is a work-related trip outside the workplace. ['Home','Workplace','Business','School','Other'] 56,208,1 48,12 2,704,209,476,464 ['Home','Workplace','Business','School','Other'] The place where the trip ends. copyindex(Place) 56,232,1 48,12 2,120,130,416,303,0,MIDM ['Home','Workplace','Business','School','Other'] The modes of transportation. ['Kevyt liikenne','Joukkoliikenne','Henkilšauto','Muu'] 64,320,1 48,12 2,102,90,476,446 ['Kevyt liikenne','Joukkoliikenne','Henkilšauto','Muu'] Time of day ['Morning','Day','Afternoon','Evening','Night'] 504,128,1 48,12 2,102,90,476,423 min/h Time spent in personal car traffic in Helsinki. Based on personal diaries of adult subjects in Expolis study in 1996-97. Table(hour)( 0.5434,0.3511,0.2547,0.2885,0.1949,0.4356,1.521,4.747,5.118,2.106,1.892,1.663,1.966,1.91,2.608,3.477,6.161,5.567,3.811,2.833,2.158,1.254,0.7295,0.5768) 400,240,1 48,24 2,161,264,476,428 2,136,28,416,569,0,MIDM 2,288,21,316,544,0,MIDM 65535,52427,65534 [Index Tunti] Anu Kousa, Expolis database 12.11.2002. trips/d Car trips per day. var a:= Trips_place*Trips_place_mode*1000; a[Mode2='Henkilšauto'] 176,176,1 48,24 2,108,133,476,462 2,32,12,489,204,0,MIDM [Place,Place1] Time of day by hour Table(Hour)( 'Night','Night','Night','Night','Night','Night','Morning','Morning','Morning','Day','Day','Day','Day','Day','Day','Afternoon','Afternoon','Afternoon','Evening','Evening','Evening','Evening','Night','Night') 504,32,1 48,24 2,18,279,476,224 2,884,115,416,538,0,MIDM 2,56,66,416,303,0,MIDM 52425,39321,65535 # Number of inhabitants by district in Jan 1st, 2001. Table(Area1)( 389,10.248K,8215,882,6768,4157,11.62K,761,2407,3401,13.137K,14.569K,8705,6832,4746,10,3542,2284,15.89K,7028,11.8K,6825,3344,5755,10.28K,19K,9940,7288,12.956K,12.983K,10.358K,4523,8375,12.656K,5284,8470,13.653K,6422,8695,3549,8782,4169,11.435K,10.766K,2122,5480,7962,11.615K,10.91K,7636,5795,3710,16.146K,9493,8819,8331,11.226K,4023,8631,28.283K,5951,8259,16.458K,13.495K,12,829,9,3235,9228,6191,3145,7835,8819,16.405K,14.91K,6105,8003,15.762K,14.608K,2209,2888,12.29K,7692,3475,8069,2237,5239,8905,9199,8253,15.238K,5847,5934,1845,4671,549,3999,572,3579,9299,6466,18.695K,14.052K,2140,4118,2619,112,3145,3465,215,47,1807,10.396K,4301,11.36K,4840,2895,1346,3723,8338,2620,5403,3375,9873,12.478K,3167,4698,14.244K,9899,0) 400,32,1 48,24 2,102,90,476,492 1,216,226,703,303,0,MIDM 2,489,294,416,303,0,MIDM 65535,52427,65534 SeutuCD 02, a CD ROM database about the Helsinki area. # The number of workplaces by district Table(Area1)( 23.894K,28.844K,6227,11.46K,9798,6390,4771,3018,1284,6659,8195,8960,17.766K,4184,12.672K,4232,8797,5226,8561,11.629K,3571,17.037K,2849,3602,3469,9525,2861,2476,3305,5571,17.35K,5016,1728,4239,1053,3709,5964,1673,849,1308,1604,2162,1287,8431,2242,975,720,1853,1668,2334,538,699,1596,1333,7414,1828,1070,7452,1394,3051,893,849,1463,1481,443,1723,4068,9201,6916,2818,6321,3340,1389,2487,7270,1709,690,2794,2389,1237,3399,3463,3694,1581,7038,3254,519,832,1336,1927,2510,4198,4122,309,1681,79,2301,478,1629,3254,2826,7822,5587,2206,1529,504,3285,1814,4254,3928,9509,2633,7034,275,1063,1958,1856,2519,232,1023,346,1808,478,1358,1605,308,2012,3644,794,0) 288,32,1 48,24 2,102,90,476,548 1,248,258,713,303,0,MIDM 2,583,35,416,303,1,MIDM 65535,52427,65534 SeutuCD 02, a CD ROM database about the Helsinki area. The municipality to which each district belongs. Table(Area1)( 'Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, downtown','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Helsinki, suburbs','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Espoo, Kauniainen','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa','Vantaa',0) 184,32,1 48,24 2,102,90,476,356 2,696,93,416,532,0,MIDM 52425,39321,65535 trips/d Car trips per day by municipality and place. Several weighting factors are used to derive the numbers from the original data. var ap:= array(Place,[Inhabitants, Workplaces, Workplaces, Inhabitants, Inhabitants]); ap:= sum((if Municipality=Municipality_info then ap else 0),area1); ap:= ap/sum(ap,Municipality); var a:= ap*Car_trips; ap:= ap[Municipality=Municipality1, Place=Place1]; a:= ap*a; a:= a/sum(sum(a,Place),Place1); a:= a*Trips_municipality; a:= a/sum(sum(sum(sum(a, Municipality), Municipality1), Place), Place1); a*sum(sum(Car_trips,Place),Place1) 288,176,1 48,24 2,16,104,498,591 1,339,342,644,303,0,MIDM 2,49,67,784,245,0,MIDM [Place,Place1] [Municipality1,Municipality] [Index Suuralue] trips/h Trips by hour from one district to another district. var ap:= array(Place,[Inhabitants, Workplaces, Workplaces, Inhabitants, Workplaces]); ap:= ap/sum(ap,area1); var a:= si_pi(Trips_place_munic,ap,Municipality,area1,Municipality_info); a:= si_pi(a,ap[area1=reg1],Municipality1,reg1,Municipality_info[area1=reg1]); var va4:= Place_weight_by_hour*Time_in_traffic; va4:= va4/sum(va4,hour); a:= a*va4; a:= a/sum(sum(a,Place),Place1) *sum(sum(sum(a,Place),Place1),hour) *Time_in_traffic/sum(Time_in_traffic,hour); a:= sum(sum(a,Place),Place1); a[area1=reg] 400,176,1 48,24 2,38,32,562,688 2,571,93,540,493,0,MIDM [Reg1,Reg] Contains functions, indexes, and nodes that are used in several modules, and log nodes. jtue 2. Aprta 2004 14:19 48,24 56,144,1 48,24 1,0,0,1,1,1,0,,0, 1,708,52,544,530,17 Flexible passengers mean those who are willing to start their trip 12 min earlier to improve the trip aggregation. This increases the volume of the time point (and respectively decreases it in the next time point), which has a positive net effect on trip aggregation. Choice(Flexible_fr,1,True) 296,88,1 48,16 [Formnode Choose_flexible1, Formnode Choose_flexible2] 52425,39321,65535 ['item 1'] The areal coverage of composite traffic can be defined in two ways. First, all requested trips within a certain area will be organised (i.e. both the origin AND the destination are inside the area). Second, all trips in the metropolitan area will be organised, if either the origin OR the destination is inside the area. The latter is denoted 'Large guarantee' in the model. That approach could be used, if an important aim is to reduce the need for an own car by offering a service that can handle most trips for those people who live in the area. The first approach is the default in the model. Choice(Large,2,True) 296,56,0 48,16 1,1,1,1,1,1,0,0,0,0 [Formnode Choose_large1, Formnode Choose_large2] 52425,39321,65535 ['item 1'] You can choose which nochange fraction(s) is (are) calculated. The number means the fraction of passengers that request a trip without a transfer. If you choose All, you will get a more thorough result, but it will take more memory and computation time, especially if 'Choose guar' or 'Choose period' are also All. Choice(Nochange_fr,1,True) 296,24,1 48,16 2,46,180,476,369 [Formnode Choose_nochange1, Formnode Choose_nochange2] 52425,39321,65535 [Choose_comp,['item 1']] [Variable Choose_comp] The transport mode: either personal car or composite traffic. ['Car','Composite'] 176,280,1 48,12 1,0,0,1,1,1,0,,0, 2,220,199,476,224 ['Car','Composite'] km The lengths of the trips shown as a frequency distribution. var comp:= aggr_period(All_trips); comp:= comp[Mode1='Composite']; var car:= comp[Mode1='Car']; var a:= array(Vehicle,[comp,0,0,0,0,car]); index length:= 1..50; for x[]:= length do ( var e:= if round(distances)=x then a else 0; e:= sum(sum(e,from),to1) ) 56,88,1 48,24 2,102,90,476,407 2,55,202,932,513,1,MIDM The areas are classified into three categories: 1) downtown (downtown of Helsinki), 2) centre (other major centres within the Metropolitan area), and 3) suburb (all other areas). Table(Self)( 1,2,3) ['Downtown','Centre','Suburb'] 56,16,1 48,12 2,10,257,476,405 2,414,239,416,303,0,MIDM 52425,39321,65535 ['Downtown','Centre','Suburb'] km/h Average speed of traffic. 40 56,312,1 48,24 2,93,231,476,322 65535,52427,65534 Index of travel type (vehicle type including the number of changes). ['Bus no change','Bus one change','Cab no change','Cab one change','Cab non-full','Car'] 176,192,1 48,12 1,1,1,1,1,1,0,,0, 2,186,152,476,399 ['Bus no change','Bus one change','Cab no change','Cab one change','Cab non-full','Car'] Functions that are used in several modules of this model, or in several models. It is therefore practical to place them into one module. jtue 2. Aprta 2004 14:47 48,24 56,200,1 48,24 1,109,108,82,340,21 (in,out:prob;classes) Toistaiseksi Variation1 ei toimi, jos classes on indeksi. TŠmŠn voi koettaa ratkaista siten, ettŠ tehdŠŠn isompi indeksi, jossa concatataan kaikki eripituiset varia-indeksit, sortataan suuruusjŠrjestykseen, ja slicataan pienemmŠt indeksit siihen. TŠmŠn lisŠksi tŠytyy linearinterp-funktiolla luoda puuttuviin kohtiin lukuja, jossa funktio kulkee nŠtisti. Nyt tŠtŠ ei ruveta tekemŠŠn. for x[]:= classes do ( index varia:= sequence(1/x,1,1/x); var c:= rank(sample(in),run); c:= ceil(c*x/samplesize)/x; var a:= if c=Varia then sample(out) else 0; var b:= if c=Varia then 1 else 0; a:= sum(a,run)/sum(b,run); if isnan(a) then 0 else a ) 168,208,1 48,24 2,57,16,476,648 in,out,classes (out:prob;deci:indextype;input:prob;input_ind:indextype;luokkia) Versio 1. index a:= ['Total VOI']; index variable:= concat(a,input_ind); for x[]:= luokkia do ( index varia:= sequence(1/x,1,1/x); var in:= ceil(rank(input,run)*x/samplesize)/x; var ncuu:= min(mean(sample(out)),deci); var d:= (if a='Total VOI' then mean(min(sample(out),deci))-ncuu else 0); var evpi:= if in=Varia then out else 0; evpi:= sum(min(mean(evpi),deci),varia)-ncuu; concat(d,evpi,a,input_ind,variable) ) 56,208,1 48,24 2,474,71,476,546 out,deci,input,input_ind,luokkia h Time unit in hours. (Should equal the acceptable waiting time.) 1/(size(time)/24) 56,96,1 48,24 1,1,1,1,1,1,0,,0, 52425,39321,65535 (Trips,Delay) time units shifts travels forward and backward in time. This is the way how travel times are taken into account. Trips = number of trips traveled at each time point. Delay = Travel time as number of time units. If delay is negative, the result is earlier in time than Trip. Time_order = a helper variable containing the rank number of each time point. var time_order:= time/time_unit+1; var a:= Time_order-Delay; var b:= (if a >max(Time_order,time) or a< min(Time_order,time) then 1 else a); slice(Trips,time,b) 168,96,1 48,24 1,1,1,1,1,1,0,,0, 2,367,75,476,512 Trips,Delay (data) index in1:= 1..size(data); var b:= slice(data,in1); var c:= unique(b,in1); b:= slice(b,in1,c); b:= slice(b,in1); c:= subset(istext(b)); b:= slice(b,in1,c); index a:= 1..size(b); slice(b,a) 168,32,1 48,24 2,102,90,476,389 data (d;roa:indextype) index etappi:= 1..max((textlength(d)+1)/5,roa); index a:= 1..size(d)*2; var c:= for x[]:= d do slice(Splittext(x,','),Etappi); c:= (if istext(c) then c else ''); var b:= c[Etappi=1]; var x:=2; while x<= size(Etappi) do ( b:= (if c[Etappi=x] = '' then b else c[Etappi=x] & ',' & b); x:= x+1); c:= concat(d,b,roa,roa,a); c 56,32,1 48,24 d,roa (a) var b:= if time>=6 and time<20 then a else 0; b:= sum(b,time); var c:= if time>=20 and time<24 then a else 0; c:= sum(c,time); var d:= if time>=24 or time<6 then a else 0; d:= sum(d,time); array(Period_1,[b,c,d]) 56,152,1 48,24 2,253,78,476,311 a (in,out:prob;classes) Toistaiseksi Variation1 ei toimi, jos classes on indeksi. TŠmŠn voi koettaa ratkaista siten, ettŠ tehdŠŠn isompi indeksi, jossa concatataan kaikki eripituiset varia-indeksit, sortataan suuruusjŠrjestykseen, ja slicataan pienemmŠt indeksit siihen. TŠmŠn lisŠksi tŠytyy linearinterp-funktiolla luoda puuttuviin kohtiin lukuja, jossa funktio kulkee nŠtisti. Nyt tŠtŠ ei ruveta tekemŠŠn. for x[]:= classes do ( index varia:= sequence(1/x,1,1/x); var c:= rank(sample(in),run); c:= ceil(c*x/samplesize)/x; var a:= if c=Varia then sample(out) else 0; var b:= if c=Varia then 1 else 0; a:= sum(a,run)/sum(b,run); if isnan(a) then 0 else a ) 168,152,1 48,24 in,out,classes (param1,sigdigits) var a:= floor(logten(param1)); var b:= param1/10^(a+1-sigdigits); round(b)*10^(a+1-sigdigits) 272,32,1 48,24 param1,sigdigits Use this library to see which variables and functions are taking most of the computation time when running your model. This library requires Analytica Enterprise, or ADE. It will not work for other versions of Analytica. Here's how to use the library: 1. First run your model, i.e. show (and therefore compute) results for the outputs you are interested in timing. 2. Click Timing "Result" button to show an array showing how long it took to evaluate each variable (in CPU seconds), ordered to show the largest times first. If you want to time additional calculations, added to existing timings. 3. Make those calculations by showing results for those variables. 4. Click button "Recompute Timings" 5. Click Timing "Result" button again. If you want to time additional calculations, starting from zero again. 6. Change relevant inputs to cause their dependents to need to be recomputed. 7. Click "Reset Timings" to set to zero. 8. Show results for outputs of interest. 9. Click Timing "Result" again to see new timings. Lonnie Chrisman Sun, Jul 13, 2003 12:18 PM indirect Sun, Sep 14, 2003 7:20 AM 48,24 56,144,1 48,24 1,1,1,1,1,1,0,0,0,0 1,433,261,-4636,266,21 2,90,44,476,224 (m: TextType) Returns a list including module m and all its descendants, i.e. objects (variables, functions, and modules) contained in m - and in any modules it contains, recursively. VAR res := [m]; VAR c := contains OF m ; IFONLY IsUndef(c) THEN res ELSE BEGIN FOR v := c Do BEGIN VAR d := Descendant_objects(Identifier OF v); res := Concat(res, d); 0 END; res END 80,176,1 52,24 2,97,125,476,394 m 1 (m: TextType) sec Returns an array of the computation time (in seconds) taken to evaluate each variable (or user-defined function). Results exclude time spent evaluating each variable's inputs. Times are sorted in descending order to show the variables taking the most time at the top. The result is indexed by .objects, a local index containing only those variables with a nonzero computation time. This function is useful for profiling a computationally intensive model to find where the time is being spent. The time includes all time spent in computing each variable since the model was opened, or since the last call to "Reset Timings". INDEX allobjs := Descendant_Objects(m); VAR allTimings := (FOR obj:=allobjs DO EvaluationTime OF obj ); INDEX UnsortedNodes := Subset(allTimings > 0); VAR timings := allTimings[allobjs = UnsortedNodes]; INDEX objects := sortIndex(-timings, UnsortedNodes); timings[UnsortedNodes = objects] 200,176,1 60,24 2,88,-2,481,571 m CPU Sec Returns an array with the evaluation Time spent in each variable and function. /* First, determine which node is the "root" node of the model */ VAR m := Identifier OF Isin OF Self ; VAR top := WHILE (NOT IsUndef(Isin OF m )) DO m := Identifier OF Isin OF m ; Computation_profile(top) 328,176,1 48,24 2,723,11,247,592,0,MIDM [Formnode Whole_model_computat, Formnode Timing_profile1] 1,F,10,3,0,0 This clears the previously cached computation for Whole_model_profile so that if additional computations have occurred since that result was displayed, the results of those new computations are reflected in the results. 80,40,1 48,24 2,95,609,476,224 Zeros all elapsed timings and resets the whole model computation profile to be recomputed to reflect (re-)evaluation of model variables. 80,96,1 48,24 1 256,40,1 124,16 1,0,0,1,0,0,0,72,0,1 Timing_profile (m: TextType) sec Returns an array of the computation time (in seconds) taken to evaluate each variable (or user-defined function). Results exclude time spent evaluating each variable's inputs. Times are sorted in descending order to show the variables taking the most time at the top. The result is indexed by .objects, a local index containing only those variables with a nonzero computation time. This function is useful for profiling a computationally intensive model to find where the time is being spent. The time includes all time spent in computing each variable since the model was opened, or since the last call to "Reset Timings". INDEX allobjs := Descendant_Objects(m); VAR allTimings := (FOR obj:=allobjs DO EvaluationTimeAll OF obj ); INDEX UnsortedNodes := Subset(allTimings > 0); VAR timings := allTimings[allobjs = UnsortedNodes]; INDEX objects := sortIndex(-timings, UnsortedNodes); timings[UnsortedNodes = objects] 200,240,1 60,24 2,102,90,529,521 m CPU Sec This displays the Time spent in each variable and function /* First, determine which node is the "root" node of the model */ VAR m := Identifier OF Isin OF Self ; VAR top := WHILE (NOT IsUndef(Isin OF m )) DO m := Identifier OF Isin OF m ; Computation_profile_(top) 328,240,1 48,24 2,655,142,407,516,0,MIDM 1,F,10,3,0,0 Area number of the origin. Equals the Area 129 coding (plus 1000) by Helsinki Metropolitan Area Council. 1001..1129 176,80,1 48,12 2,518,124,476,424 [Formnode Mista2] Area number of the destination. Equals the Area 129 coding (plus 1000) by Helsinki Metropolitan Area Council. copyindex(From) 176,104,1 48,12 An index for areal data tables. Transformed to 'From' index. 1001..1129 176,24,1 48,12 2,446,194,476,288 An index for areal data tables. Transformed to 'To' index. 1001..1129 176,48,1 48,12 The number of area. Equals the Area 129 coding (plus 1000) by Helsinki Metropolitan Area Council. [1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130] 176,136,1 48,12 2,531,226,476,224 [1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130] The names of the larger regions used in the model. ['+LŠnsi-Espoo','+Pohjois-Espoo','+EtelŠ-Espoo','+Keski-Espoo','+LŠnsi-Vantaa','+Keski-Vantaa','+Pohjois-Vantaa','+ItŠ-Vantaa','+Kanta-Helsinki','+LŠnsi-Helsinki','+Vanha-Helsinki','+Konalanseutu','+Pakilanseutu','+Malminseutu','+ItŠ-Helsinki'] 176,160,1 48,12 2,470,236,476,365 ['+LŠnsi-Espoo','+Pohjois-Espoo','+EtelŠ-Espoo','+Keski-Espoo','+LŠnsi-Vantaa','+Keski-Vantaa','+Pohjois-Vantaa','+ItŠ-Vantaa','+Kanta-Helsinki','+LŠnsi-Helsinki','+Vanha-Helsinki','+Konalanseutu','+Pakilanseutu','+Malminseutu','+ItŠ-Helsinki'] The placeholder for the composite traffic. This is used when an argument is linked to composite traffic in general, and there is no obvious node to which it can be linked. 0 56,256,1 48,24 Morning-day, evening, and night are looked at separately. table(Period_1)(1,2,3) 56,48,1 48,12 52425,39321,65535 Index of travel type (vehicle type including the number of changes). This index is the same as Vehicle except that there is an additional row, No-change trips. This is the number of trips that are forced not to be divided into two parts. Note that these trips are included in other rows, and therefore this index must not be summed up. ['Bus no change','Bus one change','Cab no change','Cab one change','Cab non-full','Car','No-change'] 176,240,1 48,12 1,1,1,1,1,1,0,,0, ['Bus no change','Bus one change','Cab no change','Cab one change','Cab non-full','Car','No-change'] 1 176,472,1 160,12 1,0,0,1,0,0,0,72,0,1 Timing_profile 0 176,448,1 160,12 1,0,0,1,0,0,0,72,0,1 From 0 172,348,1 156,12 1,0,0,1,0,0,0,72,0,1 Subsidise_groups_ 0 172,372,1 156,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Choose_large 0 172,396,1 156,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Choose_nochange 0 172,420,1 156,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Choose_flexible This module creates the node Route matrix, which contains the driving instructions from all areas to all other areas. Distances calculates the distances (by road) between the areas. To make the construction of Route matrix as simple as possible for a new city, the roads are defined in the following way. First, the whole metropolitan are is divided into 15 regions, and these regions are further divided into 129 areas with 7300 inhabitants on average. The 129 areas are standard areas for urban planning, but the regions were formed for this particular purpose. The criteria for forming a region were that they 1) are exclusive and mutually exhaustive 2) are as large as possible without creating very unrealistic routes between areas. Routes are defined in a way that between any two regions, there is only one specific road that is used to cross the region borders (and travel the distance between the regions if they are not neighbours). It is thus necessary to describe the routes between all areas within each region, and the routes between all regions. However, then it is possible to deduce the detailed routes between two areas that are in different regions using these hierarchical instructions. The routes are described as lists of areas that are along the road between the origin and destination. The route description needs not be in full detail if the details between two areas are defined in Roads node. A minimum number of existing roads were selected so that the routes in the model would not be very unrealistic. This work was done manually with a map. Note that the absolute numbers of 'Average vehicle flow on the 30 most busy roads' are likely biased upwards because all traffic from smaller streets is packed to the major roads in the model. jtue 8. Aprta 2004 14:15 jtue 19. elota 2004 10:43 48,24 184,168,1 48,24 1,257,106,79,409,17 2,102,90,476,282 Arial, 13 The complete route instruction matrix including all relevant information. var a:= Prematrix; index e:= 1..max(max((textlength(a)+1)/5,From),To1); var g:= for x[]:= a do slice(splittext(x,','),e); g:= if g=null then 'tyhjŠ' else g; var y:= 1; while y<=size(e)-1 do ( var x:= 1; while x<= size(Road_mirror) do ( var h:= Road_mirror[.a=x]; var b:= g[.e=y]; var c:= g[.e=y+1]; var d:= findintext(b,h); var f:= findintext(c,h); a:= if d>0 and f>0 and f>d then Textreplace(a,b&','&c,selecttext(h,d,f+3),true) else a; x:=x+1); y:=y+1); a 288,200,1 48,24 2,478,35,476,480 2,70,80,784,348,0,MIDM [To1,From] km The length of each origin-destination trip. var b:= for x[]:= route_matrix do ( var a:= if findintext(links_1,x)>0 then link_length1 else 0; sum(a,links_1) ); b + in_area_distance[area1=From] + in_area_distance[area1=To1] 400,200,1 48,24 2,32,14,476,521 2,27,18,883,552,0,MIDM [To1,From] km The distance that is travelled within an area collecting people before the actual trip to another area starts. Distances are rough estimates measured with a string and a ruler. This approach was considered exact enough, as the road structure is the same in all scenarios considered. Note that although not quite realistic, this value is the same for both composite and car traffic. Table(Area1)( 1,1,0.6,0.6,0.6,1,1,0.1,1,1,1,1,1.5,1.5,1,1,0.6,0.6,1,1,0.6,0.6,1,1.5,1.5,2.5,1.5,1.5,1.5,2.5,1.5,1.5,1.5,1.5,2.5,1.5,1.5,1.5,1.5,1,1.5,1.5,1.5,2.5,1.5,1,2.5,1.5,1.5,2.5,1.5,1,4,1.5,1.5,2.5,1.5,1.5,1,2.5,1.5,1.5,1.5,2.5,1.5,0.6,0.6,1.5,1.5,1.5,1.5,2.5,2.5,2.5,2.5,2.5,2.5,1.5,2.5,2.5,0.6,1.5,1.5,1.5,1.5,1,1.5,2.5,2.5,2.5,4,4,4,8,2.5,4,4,4,8,2.5,1.5,2.5,2.5,2.5,4,8,4,1.5,1.5,1.5,1,2.5,1.5,1.5,1.5,1.5,1.5,2.5,2.5,1.5,1.5,2.5,1.5,4,2.5,2.5,4,4,4,0) 400,32,1 48,24 2,148,93,416,561,0,MIDM 65535,52427,65534 Data based on a map of Helsinki Metropolitan area (YTV liikenne: PŠŠkaupunkiseudun joukkoliikennekartta 11.8.2002). The name of each area. Table(Area1)( 'Kluuvi','Kamppi','Punavuori','Kaartinkaupunki','Kruunuhaka','Katajanokka','Kaivopuisto','Munkkisaari','Ruoholahti','Salmisaari','Etu-Tššlš','Taka-Tššlš','Meilahti','Ruskeasuo','LŠnsi-Pasila','Pohjois-Pasila','ItŠ-Pasila','Hakaniemi','Kallio','SšrnŠinen','Alppila','Vallila','Hermanni','Arabianranta','KŠpylŠ','Lauttasaari','Munkkiniemi','Munkkivuori','EtelŠ-Haaga','Pohjois-Haaga','PitŠjŠnmŠki','Konala','Malminkartano','KannelmŠki','Hakuninmaa','Maunula','Patola','LŠnsi-Pakila','PaloheinŠ','ItŠ-Pakila','PukinmŠki','Viikki','PihlajamŠki','Malmi','Malmin lentokenttŠ','Tapanila','Tapaninvainio','SiltamŠki','Tapulikaupunki','Puistola','JakomŠki','Kulosaari','Laajasalo','Roihuvuori','Herttoniemenranta','Herttoniemi','Puotila','Puotinharju','Myllypuro','Kontula','Vartioharju','MellunmŠki','Vuosaari','Kallahti','Niinisaari','Suomenlinna','Keilaniemi','Otaniemi','Tapiola','Pohjois-Tapiola','Niittykumpu','Mankkaa','Westend','MatinkylŠ','Olari','Iivisniemi','Suvisaaristo','Espoonlahti','Nšykkiš','Saunalahti','MŠkkylŠ','Lintuvaara','EtelŠ-LeppŠvaara','Laajalahti','SepŠnkylŠ','Kuninkainen','Karakallio','Laaksolahti','Viherlaakso','Kauniainen','Tuomarila','Muurala','Bembšle','Nuuksio','Kauklahti','Espoonkartano','Vanhakartano','RšylŠ','KalajŠrvi','HŠmeenkylŠ','Varisto','MyyrmŠki','Martinlaakso','Petikko','Kivistš','Seutula','Viinikkala','YlŠstš','Pakkala','Veromies','Helsinki airport','Koivuhaka','Tikkurila','Ruskeasanta','SimonkylŠ','Jokiniemi','Kuninkaala','Hakkila','PŠivŠkumpu','Havukoski','Rekola','KoivukylŠ','Ilola','Korso','Metsola','Jokivarsi','Sotunki','Hakunila','LŠnsimŠki','Vaihtopiste') 512,32,1 48,24 2,102,90,476,452 2,510,11,258,615,0,MIDM 65535,52427,65534 Modified names from the Area 129 coding by Helsinki Metropolitan Area Council. A dummy index. [1,2,3,4,5,6,7,8,9,10,11,12,13,14] 64,64,1 48,12 [1,2,3,4,5,6,7,8,9,10,11,12,13,14] A dummy index. [1,2,3,4,5,6,7,8,9,10,11,12,13,14] 64,88,1 48,12 [1,2,3,4,5,6,7,8,9,10,11,12,13,14] A list of frequently used roads. The purpose of this node is to simplify definitions in nodes Routes outside and routes inside. Table(Self)( '1078,1076,1074,1073,1067,1010,1002,1001','1093,1085,1084,1028','1104,1032,1029,1028,1027,1013,1011,1002','1105,1103,1102,1035,1034,1030,1014,1012,1001','1123,1112,1109,1040,1025,1022,1020,1001,1002,1010','1125,1127,1128,1045,1042,1024,1025,1016,1014,1029','1062,1061,1058,1054,1055,1052,1020,1018,1001','1095,1093,1097,1104,1103,1107,1110,1109,1117,1128,1129','1067,1068,1084,1083,1032,1034,1038,1040,1041,1043,1045,1060,1058','1080,1078,1076,1074,1073,1067,1068','1096,1095,1093,1094','1090,1085,1084,1083,1082','1088,1087,1083,1084','1042,1041,1047,1048','1042,1043,1044,1046,1049','1052,1055,1054,1058,1057,1063,1065','1059,1060,1062,1065','1026,1010,1002,1001,1005,1006','1008,1003,1004,1001','1008,1003,1004,1005,1006','1032,1029,1014,1016,1025,1024') [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21] 288,32,1 48,24 2,166,156,470,457,0,MIDM 2,104,114,802,486,0,MIDM 65535,52427,65534 Data based on a map of Helsinki Metropolitan area (YTV liikenne: PŠŠkaupunkiseudun joukkoliikennekartta 11.8.2002). Routes are defined in a way that between any two regions, there is only one route that is used. This route is described in this node. The route description needs not be in full detail, e.g. if a route between two areas is defined in Roads node, it is enough to define the start and end areas here. Table(In3,In4)( '+LŠnsi-Espoo,1093,1097,+Pohjois-Espoo',0,0,0,0,0,0,0,0,0,0,0,0,0, '+LŠnsi-Espoo,1093,1085,1074,+EtelŠ-Espoo','+Pohjois-Espoo,1097,1093,1085,1074,+EtelŠ-Espoo',0,0,0,0,0,0,0,0,0,0,0,0, '+LŠnsi-Espoo,1093,1085,+Keski-Espoo','+Pohjois-Espoo,1097,1093,1085,+Keski-Espoo','+EtelŠ-Espoo,1068,1084,+Keski-Espoo',0,0,0,0,0,0,0,0,0,0,0, '+LŠnsi-Espoo,1093,1104,+LŠnsi-Vantaa','+Pohjois-Espoo,1097,1104,+LŠnsi-Vantaa','+EtelŠ-Espoo,1068,1032,1104,+LŠnsi-Vantaa','+Keski-Espoo,1084,1032,1104,+LŠnsi-Vantaa',0,0,0,0,0,0,0,0,0,0, '+LŠnsi-Espoo,1093,1110,+Keski-Vantaa','+Pohjois-Espoo,1097,1104,1110,+Keski-Vantaa','+EtelŠ-Espoo,1068,1040,1109,+Keski-Vantaa','+Keski-Espoo,1084,1040,1109,+Keski-Vantaa','+LŠnsi-Vantaa,1107,1110,+Keski-Vantaa',0,0,0,0,0,0,0,0,0, '+LŠnsi-Espoo,1093,1128,1127,+Pohjois-Vantaa','+Pohjois-Espoo,1097,1128,1127,+Pohjois-Vantaa','+EtelŠ-Espoo,1068,1045,1127,+Pohjois-Vantaa','+Keski-Espoo,1084,1045,1127,+Pohjois-Vantaa','+LŠnsi-Vantaa,1107,1128,1127,+Pohjois-Vantaa','+Keski-Vantaa,1109,1128,1127,+Pohjois-Vantaa',0,0,0,0,0,0,0,0, '+LŠnsi-Espoo,1093,1128,+ItŠ-Vantaa','+Pohjois-Espoo,1097,1128,+ItŠ-Vantaa','+EtelŠ-Espoo,1068,1045,1128,+ItŠ-Vantaa','+Keski-Espoo,1084,1045,1128,+ItŠ-Vantaa','+LŠnsi-Vantaa,1107,1128,+ItŠ-Vantaa','+Keski-Vantaa,1109,1117,1128,+ItŠ-Vantaa','+Pohjois-Vantaa,1127,1128,+ItŠ-Vantaa',0,0,0,0,0,0,0, '+LŠnsi-Espoo,1093,1028,1011,+Kanta-Helsinki','+Pohjois-Espoo,1097,1104,1011,+Kanta-Helsinki','+EtelŠ-Espoo,1067,1010,+Kanta-Helsinki','+Keski-Espoo,1084,1028,1011,+Kanta-Helsinki','+LŠnsi-Vantaa,1102,1001,+Kanta-Helsinki','+Keski-Vantaa,1109,1001,+Kanta-Helsinki','+Pohjois-Vantaa,1127,1025,1001,+Kanta-Helsinki','+ItŠ-Vantaa,1128,1025,1001,+Kanta-Helsinki',0,0,0,0,0,0, '+LŠnsi-Espoo,1093,1028,+LŠnsi-Helsinki','+Pohjois-Espoo,1097,1104,1029,+LŠnsi-Helsinki','+EtelŠ-Espoo,1068,1084,1028,+LŠnsi-Helsinki','+Keski-Espoo,1084,1028,+LŠnsi-Helsinki','+LŠnsi-Vantaa,1102,1014,+LŠnsi-Helsinki','+Keski-Vantaa,1109,1025,1016,+LŠnsi-Helsinki','+Pohjois-Vantaa,1127,1016,+LŠnsi-Helsinki','+ItŠ-Vantaa,1128,1016,+LŠnsi-Helsinki','+Kanta-Helsinki,1001,1012,+LŠnsi-Helsinki',0,0,0,0,0, '+LŠnsi-Espoo,1093,1028,1029,1025,+Vanha-Helsinki','+Pohjois-Espoo,1097,1104,1029,1025,+Vanha-Helsinki','+EtelŠ-Espoo,1068,1032,1025,+Vanha-Helsinki','+Keski-Espoo,1084,1028,1029,1025,+Vanha-Helsinki','+LŠnsi-Vantaa,1102,1014,1025,+Vanha-Helsinki','+Keski-Vantaa,1109,1025,+Vanha-Helsinki','+Pohjois-Vantaa,1127,1024,+Vanha-Helsinki','+ItŠ-Vantaa,1128,1024,+Vanha-Helsinki','+Kanta-Helsinki,1001,1018,+Vanha-Helsinki','+LŠnsi-Helsinki,1016,1025,+Vanha-Helsinki',0,0,0,0, '+LŠnsi-Espoo,1093,1084,1032,+Konalanseutu','+Pohjois-Espoo,1097,1104,1032,+Konalanseutu','+EtelŠ-Espoo,1068,1032,+Konalanseutu','+Keski-Espoo,1084,1032,+Konalanseutu','+LŠnsi-Vantaa,1102,1035,+Konalanseutu','+Keski-Vantaa,1109,1040,1034,+Konalanseutu','+Pohjois-Vantaa,1127,1045,1034,+Konalanseutu','+ItŠ-Vantaa,1128,1045,1034,+Konalanseutu','+Kanta-Helsinki,1001,1030,+Konalanseutu','+LŠnsi-Helsinki,1014,1030,+Konalanseutu','+Vanha-Helsinki,1025,1014,1030,+Konalanseutu',0,0,0, '+LŠnsi-Espoo,1093,1084,1032,1038,+Pakilanseutu','+Pohjois-Espoo,1097,1104,1032,1038,+Pakilanseutu','+EtelŠ-Espoo,1068,1038,+Pakilanseutu','+Keski-Espoo,1084,1038,+Pakilanseutu','+LŠnsi-Vantaa,1102,1034,1038,+Pakilanseutu','+Keski-Vantaa,1109,1040,+Pakilanseutu','+Pohjois-Vantaa,1127,1045,1040,+Pakilanseutu','+ItŠ-Vantaa,1128,1045,1040,+Pakilanseutu','+Kanta-Helsinki,1001,1020,1040,+Pakilanseutu','+LŠnsi-Helsinki,1014,1030,1034,1038,+Pakilanseutu','+Vanha-Helsinki,1025,1040,+Pakilanseutu','+Konalanseutu,1034,1038,+Pakilanseutu',0,0, '+LŠnsi-Espoo,1093,1084,1041,+Malminseutu','+Pohjois-Espoo,1097,1104,1032,1041,+Malminseutu','+EtelŠ-Espoo,1068,1041,+Malminseutu','+Keski-Espoo,1084,1041,+Malminseutu','+LŠnsi-Vantaa,1102,1034,1041,+Malminseutu','+Keski-Vantaa,1109,1040,1041,+Malminseutu','+Pohjois-Vantaa,1127,1045,+Malminseutu','+ItŠ-Vantaa,1128,1045,+Malminseutu','+Kanta-Helsinki,1001,1020,1040,1041,+Malminseutu','+LŠnsi-Helsinki,1014,1030,1034,1041,+Malminseutu','+Vanha-Helsinki,1025,1040,1041,+Malminseutu','+Konalanseutu,1034,1041,+Malminseutu','+Pakilanseutu,1040,1041,+Malminseutu',0, '+LŠnsi-Espoo,1093,1084,1045,1060,+ItŠ-Helsinki','+Pohjois-Espoo,1097,1104,1032,1045,1060,+ItŠ-Helsinki','+EtelŠ-Espoo,1068,1045,1060,+ItŠ-Helsinki','+Keski-Espoo,1084,1045,1060,+ItŠ-Helsinki','+LŠnsi-Vantaa,1102,1034,1045,1060,+ItŠ-Helsinki','+Keski-Vantaa,1109,1040,1045,1060,+ItŠ-Helsinki','+Pohjois-Vantaa,1127,1045,1060,+ItŠ-Helsinki','+ItŠ-Vantaa,1128,1045,1060,+ItŠ-Helsinki','+Kanta-Helsinki,1001,1020,1052,+ItŠ-Helsinki','+LŠnsi-Helsinki,1014,1030,1034,1045,1060,+ItŠ-Helsinki','+Vanha-Helsinki,1020,1052,+ItŠ-Helsinki','+Konalanseutu,1034,1045,1060,+ItŠ-Helsinki','+Pakilanseutu,1040,1045,1060,+ItŠ-Helsinki','+Malminseutu,1045,1060,+ItŠ-Helsinki' ) 64,32,1 48,24 2,560,183,476,508 2,70,2,872,335,0,MIDM 2,198,39,805,439,0,MIDM 65535,52427,65534 [In3,In4] [In3,In4] Data based on a map of Helsinki Metropolitan area (YTV liikenne: PŠŠkaupunkiseudun joukkoliikennekartta 11.8.2002). Changes the Routes outside into a one-dimensional list. var c:= if Routes_outside=0 then 0 else 1; c:= sum(sum(c,in3),in4); Index a:= 1..c; Index b:= ['In3','In4','Data']; var d:= Mdarraytotable(Routes_outside,a,b); d:= d[.b='Data']; d 64,136,1 48,24 2,102,90,476,297 2,214,56,537,610,0,MIDM 'Explodes' the regional route lists in a way that any driving instruction that applies to a region, applies also to all areas within the region. var b:= route_list; var x:= 1; while x<= size(Region) do ( {KŠy lŠpi jokainen alue} var c:= slice(Region,x); var d:= slice(Regions,x); var h:= b; var j:= size(b); d:= splittext(d,','); var y:= 1; while y<= size(b) do ( {KŠy lŠpi jokainen ajo-ohje} var f:= slice(h,y); f:= if Istext(f) then f else ''; (if findintext(c,f)>0 then ( f:= textreplace(f,c,d,true); {Korvaa ryhmŠaluenimet aluenimillŠ} b:= concat(b,f)) else 0); y:= y+1); x:= x+1); {TŠstŠ alkaa vanha Aluerajaytys_b} index In3:= 1..size(b); b:= slice(b, In3); b:= (if findintext('+',b)>0 then null else b); { HŠvitetŠŠn aluenimet var c:= unique(b,in3); Romauta kaikki toistuvat rivit b:= slice(b,in3,c); b:= slice(b,in3);} var c:= subset(istext(b)); {Romauta kaikki tyhjŠt rivit} b:= slice(b,in3,c); index in5:= 1..size(b); b:= slice(b,in5); {Poista reitistŠ samat toistuvat pisteet x:=1; while x<=size(mista) do ( c:= slice(mista,x)&''; b:= textreplace(b,c&','&c,c,true); x:=x+1 ); b} 64,200,1 48,24 2,102,90,476,590 2,10,11,474,620,0,MIDM Describes the small areas that belong into each larger region. The region names must start with '+'. All areas must be mentioned exactly once. Regions were selected in a way that they are as large as possible without creating very unrealistic routes between areas. Table(Region)( '1091,1092,1093,1094,1095,1096','1097,1098,1099','1067,1068,1069,1070,1071,1073,1074,1075,1076,1077,1078,1079,1080','1072,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090','1100,1101,1102,1103,1104,1105,1106,1107,1108','1109,1110,1111,1112,1113,1114,1115,1116,1123','1118,1119,1120,1121,1122,1124,1125,1126,1127','1117,1128,1129','1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1026,1066','1012,1013,1014,1015,1016,1027,1028,1029','1017,1018,1019,1020,1021,1022,1023,1024,1025','1030,1031,1032,1033,1034,1035','1036,1037,1038,1039,1040','1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051','1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065') 64,264,1 48,24 2,88,98,771,523,0,MIDM 2,20,224,651,394,0,MIDM 52425,39321,65535 Data based on a map of Helsinki Metropolitan area (YTV liikenne: PŠŠkaupunkiseudun joukkoliikennekartta 11.8.2002). The crude route instruction matrix without the information from Routes inside and Roads nodes. var mirror2:= mirror(region_explode,region_explode.in5); var a:= mirror2; a:= evaluate(selecttext(a,1,4))*10000+evaluate(selecttext(a,textlength(a)-3)); index b:=a; index c:= [1]; index d:= 2..size(Mirror2.a); var e:= concat(c,d,c,d,b); e:= slice(Mirror2,Mirror2.a,e); e:= e[.b=From*10000+To1]; if e=null then From&','&To1 else e 176,200,1 48,24 2,102,90,476,586 2,212,13,759,604,0,MIDM [To1,From] 1,I,4,2,0,0 'Mirrors' the driving instructions in a way that if an instruction applies to 'from A to B', its reverse applies to 'from B to A'. index roa:= 1..size(route_list1)+size(roads); var a:= concat(roads,route_list1,roads,route_list1.a,roa); a:= mirror(a,roa); a:= clean_rows(a); var c:= for y[]:= a do ( var e:= (if findintext(y,a)>0 then 1 else 0); e:= sum(e,e.a)-1 ); a:= if c>0 then 0 else a; clean_rows(a) 288,136,1 48,24 2,102,90,476,409 2,219,-3,563,627,0,MIDM Defines the routes between every two areas within a region. The route description needs not be in full detail, e.g. if a route between two areas is defined in Roads node, it is enough to define the start and end areas here. A minimum number of existing roads were selected so that the routes in the model would not be very unrealistic. This work was done manually with a map. Table(In3,In4,region)( '1091,1092','1097,1098','1067,1068','1072,1085,1084,1083,1081','1100,1101','1109,1110','1118,1127,1119','1117,1128','1001,1002','1012,1013','1017,1019,1018','1030,1034,1031','1036,1037','1041,1042','1052,1055,1053', 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, '1091,1092,1093','1097,1099','1067,1069','1072,1085,1084,1083,1082','1100,1101,1102','1109,1110,1111','1118,1120','1117,1128,1129','1001,1004,1003','1012,1014','1017,1019','1030,1034,1032','1036,1038','1041,1043','1052,1055,1054', '1092,1093','1098,1097,1099','1068,1069','1081,1082','1101,1102','1110,1111','1119,1127,1120','1128,1129','1002,1004,1003','1013,1014','1018,1019','1031,1032','1037,1036,1038','1042,1043','1053,1055,1054', 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, '1091,1092,1093,1094',0,'1067,1073,1070','1072,1085,1084,1083','1100,1104,1103','1109,1112','1118,1120,1121','1117,1130','1001,1004','1012,1015','1017,1020','1030,1034,1032,1033','1036,1038,1039','1041,1044','1052,1055', '1092,1093,1094',0,'1068,1070','1081,1083','1101,1103','1110,1109,1112','1119,1121','1128,1130','1002,1004','1013,1015','1018,1020','1031,1032,1033','1037,1040,1039','1042,1043,1044','1053,1055', '1093,1094',0,'1069,1070','1082,1083','1102,1103','1111,1112','1120,1121','1129,1130','1003,1004','1014,1016,1015','1019,1020','1032,1033','1038,1039','1043,1044','1054,1055', 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, '1091,1092,1095',0,'1067,1073,1071','1072,1085,1084','1100,1104','1109,1113','1118,1120,1122',0,'1001,1005','1012,1015,1016','1017,1021','1030,1034','1036,1038,1040','1041,1043,1045','1052,1055,1056', '1092,1095',0,'1068,1070,1071','1081,1083,1084','1101,1104','1110,1109,1113','1119,1121,1122',0,'1002,1001,1005','1013,1015,1016','1018,1019,1021','1031,1034','1037,1040','1042,1045','1053,1055,1056', '1093,1095',0,'1069,1071','1082,1083,1084','1102,1101,1104','1111,1110,1109,1113','1120,1122',0,'1003,1004,1005','1014,1016','1019,1021','1032,1034','1038,1040','1043,1045','1054,1056', '1094,1093,1095',0,'1070,1071','1083,1084','1103,1104','1112,1113','1121,1122',0,'1004,1005','1015,1016','1020,1021','1033,1032,1034','1039,1040','1044,1043,1045','1055,1056', 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, '1091,1092,1095,1096',0,'1067,1073','1072,1085','1100,1104,1103,1105','1109,1112,1114','1118,1120,1121,1124',0,'1001,1005,1006','1012,1013,1027','1017,1022','1030,1034,1035',0,'1041,10441046','1052,1055,1054,1058,1057', 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0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 ) 176,32,1 48,24 2,109,4,872,346,0,MIDM 2,184,194,805,439,0,MIDM 65535,52427,65534 [In3,In4] [In3,In4] Data based on a map of Helsinki Metropolitan area (YTV liikenne: PŠŠkaupunkiseudun joukkoliikennekartta 11.8.2002). Changes the Routes inside into a one-dimensional list. var c:= if Routes_inside=0 then 0 else 1; c:= sum(sum(sum(c,in3),in4),region); Index f:= 1..c; Index b:= ['Region','In3','In4','Data']; var d:= Mdarraytotable(Routes_inside,f,b); d:= d[.b='Data']; d:= if textlength(d)=9 then 0 else d; clean_rows(d) 176,136,1 48,24 2,283,96,476,346 2,49,13,370,610,0,MIDM km The distance between two areas. Distances are rough estimates measured with a string and a ruler. This approach was considered exact enough, as the road structure is the same in all scenarios considered. Table(Links_1)( 0,0.8,1,0.6,1.7,1.1,1.2,1.5,1.7,1.2,0.8,0.8,0.8,2.1,2.9,2.2,1.1,0.8,3,2.4,1.1,1.3,3.3,1.7,9999,9999,3,5.4,2.8,3,3.6,3,2,2.6,1,9999,1.6,9999,1.2,1.4,1,9999,9999,1,9999,2.3,2.2,1.1,1.6,1.8,9999,1.1,1.4,1.2,1,1.1,1.7,0.8,0.9,9999,2.4,1.6,2.3,0.4,1.8,2.4,1.9,2.5,9999,9999,4,0.8,9999,1.3,9999,3.7,9999,2.8,1.8,1.9,2.5,2.4,2.1,2.8,3.4,5.6,1.4,1.7,9999,1.8,1.8,0.8,2.6,2.7,1.2,2.2,2.8,5.3,1.9,1.3,1.4,2.3,2,2,1.4,1.2,4.6,3.2,1.7,1.8,4,2.9,9999,9999,9999,9999,9999,9999,9999,2.2,9999,9999,9999,9999,9999,5,2.4,3.6,1.4,2.4,1.6,3,3.2,2,1.5,1.5,2.1,1.2,1.1,2.6,9999,1.7,9999,9999,2.6,1,2.2,1.3,2.6,2.7,2.6,1.9,1.1,2.5,4,2,3,9999,3,1.3,2.7,3.2,2.6,1.7,1.1,2,0.9,2.8,1.6,2.4,1.5,2.8,1.7,2.7,1.3,1.5,3,2.8,2.7,5,2.2,3.8,4.2,3.4,5.2,4.7,1.1,5.5,1,0.9,1.2,9999,3,2.9,1.1,2.8,2.3,3.3,3,3,2.1,6.8,1.5,4.1,3.2,2,1.7,2,1.4,3.2,4.8,3.6,5.8,6.8,2.3,5.2,8.7,4.2,1.6,3.4,9999,3,5.4,4.1,3.6,5.9,5.4,3.6,2,6.8,1.6,3,6.2,2.1,4.2,1.4,2.3,4,3.6,2.8,9999,3.4,2,3.8,9999,1.8,3.4,1.8,2,9999,3.2,9999,1.5,1.2,9999,9999,9999,2.5,4.1,3,3,9999,2.9,2.3,2.2,1.8,2,1.6,2.4,3.5,2,0.9,4.7,3.6,2.8) 400,112,1 48,24 2,743,190,227,419,0,MIDM 2,288,18,177,576,0,MIDM 65535,52427,65534 1,D,4,2,0,0 Data based on a map of Helsinki Metropolitan area (YTV liikenne: PŠŠkaupunkiseudun joukkoliikennekartta 11.8.2002). Index showing the direct links that exist in reality, i.e. excluding those routes from one area to another where you have to go through a third area. ['1001,1001','1001,1002','1001,1004','1001,1005','1001,1012','1001,1018','1002,1003','1002,1004','1002,1009','1002,1010','1002,1011','1003,1004','1003,1007','1003,1008','1003,1009','1003,1010','1004,1005','1004,1007','1004,1009','1004,1010','1005,1006','1005,1007','1005,1066','1009,1010','1009,1085','1009,1089','1010,1026','1010,1067','1011,1013','1012,1013','1012,1014','1012,1015','1013,1014','1013,1015','1013,1027','1013,1030','1014,1016','1014,1017','1014,1029','1014,1030','1015,1016','1015,1017','1015,1030','1016,1025','1016,1030','1017,1019','1017,1020','1017,1021','1017,1022','1017,1025','1017,1027','1018,1019','1018,1020','1019,1020','1019,1021','1019,1022','1020,1021','1020,1022','1020,1023','1020,1028','1020,1052','1021,1022','1021,1025','1022,1023','1022,1025','1023,1024','1024,1025','1024,1042','1025,1028','1025,1032','1025,1040','1027,1028','1027,1030','1028,1029','1028,1030','1028,1084','1029,1030','1029,1032','1030,1034','1031,1032','1031,1034','1032,1033','1032,1034','1032,1035','1032,1083','1032,1104','1034,1035','1034,1038','1034,1109','1035,1102','1036,1037','1036,1038','1037,1040','1038,1039','1038,1040','1039,1040','1040,1041','1040,1109','1041,1042','1041,1043','1041,1044','1041,1047','1042,1043','1042,1045','1043,1044','1043,1045','1043,1050','1044,1045','1044,1046','1044,1047','1045,1050','1045,1051','1045,1052','1045,1053','1045,1054','1045,1055','1045,1056','1045,1057','1045,1059','1045,1060','1045,1061','1045,1062','1045,1063','1045,1064','1045,1065','1045,1128','1046,1047','1046,1048','1046,1049','1046,1050','1047,1048','1048,1049','1049,1050','1050,1051','1052,1055','1053,1055','1054,1055','1054,1056','1054,1058','1054,1059','1054,1061','1055,1056','1055,1058','1055,1061','1056,1059','1057,1058','1057,1060','1057,1061','1057,1063','1058,1059','1058,1060','1058,1061','1059,1060','1060,1061','1060,1062','1061,1062','1062,1063','1062,1064','1062,1065','1063,1064','1063,1065','1064,1065','1067,1068','1067,1069','1067,1073','1068,1069','1068,1070','1068,1084','1069,1070','1069,1071','1069,1073','1070,1071','1070,1073','1071,1073','1071,1074','1072,1085','1073,1074','1074,1075','1074,1076','1074,1085','1075,1076','1076,1077','1076,1078','1076,1079','1077,1078','1078,1079','1078,1080','1079,1080','1081,1082','1081,1083','1082,1083','1082,1084','1082,1086','1082,1087','1083,1084','1083,1086','1083,1087','1084,1085','1084,1086','1085,1086','1085,1090','1085,1093','1086,1087','1086,1090','1087,1088','1087,1089','1088,1089','1089,1090','1091,1092','1092,1093','1092,1095','1093,1094','1093,1095','1093,1097','1095,1096','1097,1098','1097,1099','1097,1104','1100,1101','1100,1104','1100,1111','1101,1102','1101,1103','1101,1104','1102,1103','1102,1107','1102,1108','1103,1104','1103,1105','1103,1106','1103,1107','1103,1108','1105,1106','1107,1108','1107,1110','1109,1110','1109,1112','1109,1113','1109,1117','1110,1111','1111,1117','1112,1113','1112,1114','1112,1115','1112,1117','1112,1123','1113,1114','1113,1115','1113,1116','1113,1117','1114,1115','1114,1117','1114,1123','1115,1116','1115,1117','1116,1117','1117,1123','1117,1128','1118,1120','1118,1127','1119,1121','1119,1125','1119,1126','1119,1127','1120,1121','1120,1122','1120,1127','1121,1122','1121,1124','1121,1125','1124,1125','1125,1126','1125,1127','1127,1128','1128,1129'] 400,144,1 48,12 1,D,4,2,0,0 ['1001,1001','1001,1002','1001,1004','1001,1005','1001,1012','1001,1018','1002,1003','1002,1004','1002,1009','1002,1010','1002,1011','1003,1004','1003,1007','1003,1008','1003,1009','1003,1010','1004,1005','1004,1007','1004,1009','1004,1010','1005,1006','1005,1007','1005,1066','1009,1010','1009,1085','1009,1089','1010,1026','1010,1067','1011,1013','1012,1013','1012,1014','1012,1015','1013,1014','1013,1015','1013,1027','1013,1030','1014,1016','1014,1017','1014,1029','1014,1030','1015,1016','1015,1017','1015,1030','1016,1025','1016,1030','1017,1019','1017,1020','1017,1021','1017,1022','1017,1025','1017,1027','1018,1019','1018,1020','1019,1020','1019,1021','1019,1022','1020,1021','1020,1022','1020,1023','1020,1028','1020,1052','1021,1022','1021,1025','1022,1023','1022,1025','1023,1024','1024,1025','1024,1042','1025,1028','1025,1032','1025,1040','1027,1028','1027,1030','1028,1029','1028,1030','1028,1084','1029,1030','1029,1032','1030,1034','1031,1032','1031,1034','1032,1033','1032,1034','1032,1035','1032,1083','1032,1104','1034,1035','1034,1038','1034,1109','1035,1102','1036,1037','1036,1038','1037,1040','1038,1039','1038,1040','1039,1040','1040,1041','1040,1109','1041,1042','1041,1043','1041,1044','1041,1047','1042,1043','1042,1045','1043,1044','1043,1045','1043,1050','1044,1045','1044,1046','1044,1047','1045,1050','1045,1051','1045,1052','1045,1053','1045,1054','1045,1055','1045,1056','1045,1057','1045,1059','1045,1060','1045,1061','1045,1062','1045,1063','1045,1064','1045,1065','1045,1128','1046,1047','1046,1048','1046,1049','1046,1050','1047,1048','1048,1049','1049,1050','1050,1051','1052,1055','1053,1055','1054,1055','1054,1056','1054,1058','1054,1059','1054,1061','1055,1056','1055,1058','1055,1061','1056,1059','1057,1058','1057,1060','1057,1061','1057,1063','1058,1059','1058,1060','1058,1061','1059,1060','1060,1061','1060,1062','1061,1062','1062,1063','1062,1064','1062,1065','1063,1064','1063,1065','1064,1065','1067,1068','1067,1069','1067,1073','1068,1069','1068,1070','1068,1084','1069,1070','1069,1071','1069,1073','1070,1071','1070,1073','1071,1073','1071,1074','1072,1085','1073,1074','1074,1075','1074,1076','1074,1085','1075,1076','1076,1077','1076,1078','1076,1079','1077,1078','1078,1079','1078,1080','1079,1080','1081,1082','1081,1083','1082,1083','1082,1084','1082,1086','1082,1087','1083,1084','1083,1086','1083,1087','1084,1085','1084,1086','1085,1086','1085,1090','1085,1093','1086,1087','1086,1090','1087,1088','1087,1089','1088,1089','1089,1090','1091,1092','1092,1093','1092,1095','1093,1094','1093,1095','1093,1097','1095,1096','1097,1098','1097,1099','1097,1104','1100,1101','1100,1104','1100,1111','1101,1102','1101,1103','1101,1104','1102,1103','1102,1107','1102,1108','1103,1104','1103,1105','1103,1106','1103,1107','1103,1108','1105,1106','1107,1108','1107,1110','1109,1110','1109,1112','1109,1113','1109,1117','1110,1111','1111,1117','1112,1113','1112,1114','1112,1115','1112,1117','1112,1123','1113,1114','1113,1115','1113,1116','1113,1117','1114,1115','1114,1117','1114,1123','1115,1116','1115,1117','1116,1117','1117,1123','1117,1128','1118,1120','1118,1127','1119,1121','1119,1125','1119,1126','1119,1127','1120,1121','1120,1122','1120,1127','1121,1122','1121,1124','1121,1125','1124,1125','1125,1126','1125,1127','1127,1128','1128,1129'] This module is only for creating the index Links_1, and now when the index has been created, these nodes are no longer needed. ktluser 3. marta 2004 16:37 48,24 400,264,1 48,24 1,40,0,-2191,294,17 var x:= 1; var a:= slice(From,x)*10000+From; while x<size(From) do ( x:= x+1; var b:= slice(From,x)*10000+From; a:= concat(a,b) ) 48,88,1 48,12 2,146,145,416,303,0,MIDM 1,I,4,2,0,0 var a:=From&','&To1; var c:= for x[]:= a do ( var b:= if findintext(x,Route_matrix)>0 then 1 else 0; sum(sum(b,From),To1) ); c:= c[From=floor(link/10000),To1=(link-floor(link/10000)*10000)]; c:= if c>0 then link else 0; c 48,56,1 48,24 2,102,90,476,283 2,44,74,598,377,0,MIDM [To1,From] 1,I,4,2,0,0 var c:= if floor(link/10000)>=(link-floor(link/10000)*10000) then 0 else links1; c:= unique(c,link); index b:= 1..size(c); c:= slice(c,b); floor(c/10000)&','&c-floor(c/10000)*10000 160,56,1 48,24 2,349,22,593,572,0,MIDM var a:= max(distances,To1); link_teko; a 272,56,1 48,24 2,216,156,476,436 'Static nodes' contains previously computed simulations in a static form. The traffic optimising is rather time-consuming work (1 hour per scenario), and it cannot be done in real time. Therefore, all health effect and cost estimates are calculated from previously computed numbers that are stored in this module. ktluser 30. lokta 2004 9:45 48,24 56,32,1 48,24 1,0,0,1,1,1,0,,0, 1,88,107,701,511,17 A table of different scenarios to be studied. Each row contains the values for the input variables used for the scenario. Table(Input_var,Scenario)( 0,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1, 7,7,7,7,7,7,7,7,7,7,7,7,7,6,6,6,6,6,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,4,4,4,4,4,4,4,4,4,4,4,4,3,3,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 ) ['Composite fraction','Guarantee level','Lim'] 288,48,1 48,24 2,344,113,476,224 2,13,32,698,642,0,MIDM 2,45,69,655,554,0,MIDM 52425,39321,65535 Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:1 Zminimum:0 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] [Input_var,Scenario] [Input_var,Scenario] Index for a list of scenarios to be modelled. [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85] 288,80,1 48,12 2,102,90,476,547 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85] The combined result of various variables using the assumptions listed in Scenarios node. Each scenario is run one by one, and the results are stored in this node. var x:= 1; var a:= 0; var c:= 0; while x<= size(scenario) do ( a:= scenarios[scenario=x]; a:= whatif(Outputs1,Scenario_input,a); c:= if scenario=x then a else c; x:= x+1); c 176,48,1 48,12 2,463,75,476,367 2,24,7,629,389,0,MIDM [Scenario,Period_1] [Index Travel_type] The output variables from the traffic optimising module: Number of passenger trips Vehicle kilometres driven Parking lots needed for the vehicles that are used Average vehicle numbers per hour for the 30 most busy links at 8.00-9.00 in the morning Number of vehicles needed Waiting time due to traffic jams and waiting for composite vehicle to arrive. ['Trips','Trips by vehicle','Vehicle km','Parking lot','Link intensity','Vehicles','Waiting'] 64,80,1 48,12 2,19,357,209,266,0,MIDM [0,0,1,0] ['Trips','Trips by vehicle','Vehicle km','Parking lot','Link intensity','Vehicles','Waiting'] sequence(0,23.99,0.2) 440,72,1 48,12 Morning-day, evening, and night are looked at separately. [' 6.00-20.00','20.00-24.00',' 0.00- 6.00'] 64,104,1 48,12 2,102,90,476,402 [' 6.00-20.00','20.00-24.00',' 0.00- 6.00'] 1 64,48,1 48,24 Outputs1 trips/h The combined result of Trips per hour using the assumptions listed in Scenarios node. Each scenario is run one by one, and the results are stored in this node. var x:= 1; var a:= 0; var c:= 0; while x<= size(scenario) do ( a:= scenarios[scenario=x]; a:= whatif(Trips_per_hour,Scenario_input,a); c:= if scenario=x then a else c; x:= x+1); c 176,24,1 48,12 2,386,142,476,476 2,479,39,629,389,1,MIDM [Time,Vehicle] var a:= 0; a:= if choose_scen = 'Article + sensitivity' then Scen1 else 0; a:= if choose_scen='Inverse guarantee' then Scen1_0_4 else a; a:= if choose_scen='Only 9 seat vehicles' then Scen_9seats else a; index scenario:= if choose_scen = 'Article + sensitivity' then copyindex(Scenario_1) else (if choose_scen='Inverse guarantee' then copyindex(Scenario1_0_1) else (if choose_scen='Only 9 seat vehicles' then copyindex(Scenario5) else [0])); a:= if choose_scen = 'Article + sensitivity' then a[Scenario_1=scenario] else a; a:= if choose_scen='Inverse guarantee' then a[Scenario1_0_1=scenario] else a; a:= if choose_scen='Only 9 seat vehicles' then a[Scenario5=scenario] else a; a 448,296,1 48,24 2,102,90,476,457 2,248,258,664,303,0,MIDM 2,32,439,1053,331,0,MIDM [Scenario1_0_1,Period_1] [Sys_localindex('SCENARIO'),Output_1] [0,0,1,0] Index for a list of scenarios to be modelled. [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85] 64,184,1 48,12 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85] A table of different scenarios to be studied. Each row contains the values for the input variables used for the scenario. var a:= 0; a:= if choose_scen = 'Article + sensitivity' then Scen1_2 else 0; a:= if choose_scen='Inverse guarantee' then Scenarios1_0_3 else a; a:= if choose_scen='Only 9 seat vehicles' then Scenarios_9_seat else a; index scenario:= if choose_scen = 'Article + sensitivity' then copyindex(Scenario_1) else (if choose_scen='Inverse guarantee' then copyindex(Scenario1_0_1) else (if choose_scen='Only 9 seat vehicles' then copyindex(Scenario5) else [0])); a:= if choose_scen = 'Article + sensitivity' then a[Scenario_1=scenario] else a; a:= if choose_scen='Inverse guarantee' then a[Scenario1_0_1=scenario] else a; a:= if choose_scen='Only 9 seat vehicles' then a[Scenario5=scenario] else a; a ['Composite fraction','Guarantee level','Lim'] 448,152,1 48,24 2,394,67,698,560,0,MIDM 2,86,188,675,554,0,MIDM 19661,48336,65535 [Input_var,Scenario] [Sys_localindex('SCENARIO'),Input_var] Table(Time_stat,Scenario1_0_1,Vehicle)( 0,0,0,0,0,13.16K, 0,0,0,0,565,12.69K, 0,0,0,20,1335,12.21K, 0,0,0,220,2435,12.14K, 0,360,0,1360,4975,10.25K, 0,1320,100,2740,5885,8115, 0,1720,40,3160,6125,7280, 0,2320,120,3840,6770,6990, 0,3120,80,4240,7070,5980, 0,4600,60,5000,7750,4585, 0,5400,200,5440,7840,3365, 0,8800,440,6040,8410,1250, 40,9360,480,6580,8735,0, 0,0,0,0,470,13.03K, 0,0,0,20,910,12.6K, 0,0,0,160,1660,12K, 0,240,0,1300,3465,11.08K, 0,1320,20,2100,4500,8985, 0,1200,20,2380,4750,8290, 0,2000,100,2540,4945,8090, 0,2120,100,3080,5360,7800, 0,3280,120,3460,5685,6890, 0,4560,220,4480,6105,5830, 0,6200,280,4680,6410,4035, 0,7880,460,5180,6625,3215, 0,0,0,0,275,13.07K, 0,0,0,40,775,12.95K, 0,0,0,60,1635,11.94K, 0,320,40,1060,3035,11.42K, 0,920,40,1880,4040,9870, 0,1080,40,2360,4455,8975, 0,1520,80,2580,4480,8920, 0,1880,60,2760,4755,8345, 0,3440,120,3500,5125,7220, 0,3600,220,3740,5685,6590, 0,5680,200,4100,5790,4605, 0,6280,420,5040,6210,4155, 0,0,0,0,110,13.06K, 0,0,0,20,550,12.8K, 0,0,0,280,1180,12.82K, 0,280,0,640,2105,11.37K, 0,720,0,1080,2925,10.75K, 0,800,20,1420,2985,10.4K, 0,960,20,1600,3150,9820, 0,1840,100,1560,3345,10.07K, 0,2240,100,2720,3390,9660, 0,2640,60,2660,3785,8170, 0,3960,280,3060,3795,7490, 40,4320,400,3680,3970,6865, 0,0,0,0,70,13.26K, 0,0,0,20,215,13.11K, 0,0,0,20,390,13.39K, 0,40,0,180,660,12.83K, 0,160,0,240,870,12.65K, 0,240,0,400,805,12.41K, 0,400,0,480,930,12.67K, 0,320,20,680,1035,12.51K, 0,440,40,720,1110,12.33K, 0,600,100,580,1140,12.31K, 0,1120,40,800,1110,11.48K, 0,1080,60,960,1190,11.74K, 0,0,0,0,35,12.87K, 0,0,0,0,90,13.11K, 0,0,0,40,175,12.95K, 0,0,0,120,360,12.88K, 0,40,0,240,485,12.79K, 0,120,0,180,565,12.95K, 0,120,0,160,575,12.57K, 0,120,0,300,565,12.61K, 0,400,0,260,565,12.82K, 0,120,40,500,670,12.52K, 0,320,60,440,655,12.72K, 0,320,60,660,705,12.23K, 0,0,0,0,5,13.68K, 0,0,0,0,10,13.21K, 0,0,0,0,75,13.22K, 0,0,0,0,90,13.69K, 0,0,0,100,160,13.05K, 0,0,0,140,165,12.81K, 0,0,0,120,230,13.06K, 0,0,0,80,270,13.33K, 0,0,20,60,270,13K, 0,0,20,100,250,12.48K, 0,80,40,100,300,13.02K, 0,0,40,160,345,13.11K, 0,0,0,0,0,12.04K, 0,0,0,0,510,12.1K, 0,0,0,40,1205,11.74K, 0,0,0,280,2220,10.84K, 0,320,0,1180,4785,9000, 0,1480,40,2480,5950,7180, 0,2080,60,2820,6125,6905, 0,2080,40,3460,6770,6180, 0,3000,40,3900,6645,5440, 0,3800,160,4980,7680,4230, 0,5240,200,4940,7745,3140, 0,7520,240,6180,8420,1195, 0,8840,520,6680,8725,0, 0,0,0,0,380,12.27K, 0,0,0,20,880,12.34K, 0,40,0,140,1585,11.49K, 0,240,20,1100,3405,10.13K, 0,840,40,2240,4540,8435, 0,1080,40,2380,4920,8230, 0,1480,20,2580,4835,7445, 0,1960,100,3080,5120,7115, 0,3120,100,3280,5765,5955, 0,4160,140,4060,6345,5280, 0,5560,140,4700,6155,3810, 0,7120,260,4860,6810,2815, 0,0,0,0,320,12.61K, 0,0,0,0,760,11.8K, 0,0,0,60,1565,11.41K, 0,280,0,980,3020,10.19K, 0,880,20,1820,4010,8720, 0,920,40,2240,4425,8555, 0,1640,60,2360,4415,8035, 0,1760,100,2920,4620,7755, 0,2600,120,3080,5165,6530, 40,3400,160,3300,5565,5890, 0,4920,240,4240,5460,4520, 40,6120,360,4460,6135,3655, 0,0,0,0,160,11.87K, 0,0,0,20,565,11.85K, 0,0,0,200,1180,11.69K, 0,240,40,540,2100,10.4K, 0,760,20,1100,2805,9860, 0,840,60,1560,2770,9490, 0,1000,0,1600,3155,9130, 40,1440,40,1700,3470,9070, 0,1760,60,2720,3480,8565, 0,2440,100,2500,3865,8155, 0,3440,240,2920,3950,6865, 40,4480,340,3220,4005,6250, 0,0,0,0,55,12.32K, 0,0,0,0,230,12.06K, 0,0,0,20,350,12.19K, 0,80,0,120,615,12.72K, 0,120,0,400,830,11.58K, 0,120,20,460,810,11.71K, 0,280,0,460,900,11.57K, 0,360,20,460,1030,11.5K, 0,280,20,800,1015,11.44K, 0,600,0,660,1080,10.86K, 0,1080,140,720,1135,10.67K, 0,1000,100,880,1190,10.81K, 0,0,0,0,45,12.77K, 0,0,0,0,90,12.37K, 0,0,0,60,115,12.82K, 0,0,0,20,375,12.05K, 0,80,0,160,475,12K, 0,0,20,220,560,12.29K, 0,80,0,220,560,11.56K, 0,80,0,280,620,12.11K, 0,240,0,380,575,11.42K, 0,120,0,520,645,11.46K, 0,200,80,520,695,11.17K, 0,400,20,480,680,11.16K, 0,0,0,0,0,12.45K, 0,0,0,0,25,12.19K, 0,0,0,0,40,11.89K, 0,0,0,0,155,11.8K, 0,0,20,60,185,12.07K, 0,0,40,40,145,12.04K, 0,0,0,20,260,11.97K, 0,0,20,60,320,12.04K, 0,40,0,40,245,11.74K, 0,40,0,80,285,11.93K, 0,40,20,160,330,11.85K, 0,0,0,140,340,11.99K, 0,0,0,0,0,11.13K, 0,0,0,20,515,10.85K, 0,0,0,40,1020,10.66K, 0,0,0,120,2135,10.09K, 0,400,0,1120,4200,8150, 0,840,60,2560,5895,6900, 0,1800,20,3020,5995,6145, 0,1680,80,3220,6275,5705, 0,2560,40,3460,6560,5155, 0,3400,80,4280,7235,4435, 0,4880,160,5060,7380,2620, 0,5880,140,5920,8400,1115, 0,8000,460,6460,8470,0, 0,0,0,0,380,11.27K, 0,0,0,0,925,10.65K, 0,0,0,100,1655,10.02K, 0,240,0,1020,3280,8830, 0,880,60,1660,4310,8130, 0,1040,0,1960,4605,7450, 0,1320,40,2400,4625,7035, 0,1880,20,2800,4860,6655, 0,2840,100,3060,5420,5665, 0,3680,100,3980,5610,4930, 0,4760,140,4320,6335,3420, 40,6320,140,4640,6295,2715, 0,0,0,0,285,11.22K, 0,0,0,0,715,10.78K, 0,0,0,80,1555,10.56K, 0,280,0,1000,2935,9095, 0,600,40,1740,3890,8370, 0,1120,0,1920,4180,7600, 0,1400,40,2280,4535,7200, 0,1720,40,2420,4775,6710, 0,2400,40,2800,4930,6310, 40,3160,160,3140,5595,5250, 0,4520,240,3700,5355,4170, 0,5320,240,4360,5745,3490, 0,0,0,0,240,11.55K, 0,0,0,20,635,10.96K, 0,0,0,160,1000,10.65K, 0,160,0,620,1915,9890, 0,520,0,1200,2685,9090, 0,920,0,1100,2880,8850, 0,1040,60,1700,2860,8570, 0,1280,40,1940,3085,8495, 0,2120,100,1960,3635,8145, 40,2160,100,2420,3735,7215, 0,3320,120,2760,3810,6415, 0,4000,160,2940,3740,5920, 0,0,0,0,35,11.75K, 0,0,0,0,185,11.25K, 0,0,0,20,320,11.1K, 0,0,0,120,655,11.12K, 0,80,0,280,850,10.84K, 0,120,0,320,830,10.92K, 0,280,0,380,860,10.67K, 0,240,40,560,870,10.41K, 0,480,20,460,1020,9610, 0,640,20,600,1095,10.1K, 0,720,40,840,1055,9805, 0,920,40,800,1095,9695, 0,0,0,0,45,11.44K, 0,0,0,0,95,10.69K, 0,0,0,0,120,11.14K, 0,0,0,40,280,11.68K, 0,0,0,160,355,11.01K, 0,80,20,120,495,10.7K, 0,40,0,300,470,10.95K, 0,0,0,340,580,10.82K, 0,200,0,200,585,10.62K, 0,320,0,220,570,10.74K, 0,240,40,520,565,10.43K, 0,440,80,520,720,10.01K, 0,0,0,0,10,11.65K, 0,0,0,0,25,11.42K, 0,0,0,0,40,11.01K, 0,0,0,0,200,10.6K, 0,0,0,20,170,11.08K, 0,0,0,40,130,10.87K, 0,0,0,40,220,10.76K, 0,0,20,100,245,11.63K, 0,0,20,80,275,11K, 0,0,0,120,310,11.27K, 0,0,20,120,310,11.21K, 0,40,20,160,360,11.02K, 0,0,0,0,0,10.17K, 0,0,0,0,460,10.02K, 0,0,0,0,945,9530, 0,0,0,120,2135,9360, 0,200,0,1100,4020,7530, 0,760,0,2000,5605,6445, 0,1680,0,2580,5885,5275, 0,1640,40,2640,5925,5385, 0,2000,40,3340,6340,4560, 0,3320,20,3900,6830,3625, 0,4000,60,4240,7435,2630, 0,5760,100,5120,8065,945, 40,7280,240,5880,7825,0, 0,0,0,0,325,10.34K, 0,0,0,0,950,9780, 0,0,0,140,1470,9600, 0,200,0,820,2965,8670, 0,600,40,1680,4100,6965, 0,720,20,1980,4355,6440, 0,1000,20,2020,4560,6280, 0,1400,60,2360,5105,5900, 0,2120,100,3160,5335,4815, 0,3040,100,3360,5925,4280, 0,4320,140,3880,6410,3315, 0,5160,180,4260,6465,2585, 0,0,0,0,265,9785, 0,0,0,0,750,10.44K, 0,0,0,200,1335,9450, 0,160,0,760,2860,8830, 0,720,0,1440,3805,7330, 0,760,60,2020,3795,7315, 0,1400,20,1640,4225,6650, 0,1280,40,2420,4495,6625, 0,1880,20,2760,4715,5565, 0,2600,140,3280,5170,5120, 0,3520,100,3560,5325,4020, 0,4600,160,4140,5780,3210, 0,0,0,0,185,10.37K, 0,0,0,0,590,9855, 0,0,0,100,910,9835, 0,120,20,460,1890,8840, 0,880,0,900,2430,8345, 0,760,0,1300,2875,7825, 0,920,20,1160,2970,7950, 0,960,60,1400,3275,7570, 0,1600,80,1900,3450,7215, 0,2000,60,2300,3530,6665, 0,2440,160,2680,3780,5830, 0,3320,140,2840,3655,5205, 0,0,0,0,55,10.16K, 0,0,0,0,160,10.16K, 0,0,0,0,330,10.12K, 0,0,0,160,610,10.22K, 0,0,20,280,700,9985, 0,0,20,240,850,9665, 0,80,0,440,885,10.16K, 0,120,0,440,1025,9475, 0,280,0,380,990,9170, 0,480,0,560,1025,8985, 0,560,40,720,1010,8770, 0,720,40,680,1105,9025, 0,0,0,0,20,10.64K, 0,0,0,0,85,10.21K, 0,0,0,0,140,9825, 0,40,0,40,320,10.22K, 0,0,0,40,405,10.37K, 0,0,0,220,420,10.35K, 0,40,0,200,550,10.15K, 0,120,0,280,450,9785, 0,120,0,200,580,9775, 0,120,20,260,605,9755, 0,360,0,400,605,9515, 0,320,40,460,735,9180, 0,0,0,0,15,10.08K, 0,0,0,0,40,10.01K, 0,0,0,0,50,10.38K, 0,0,0,0,180,10.4K, 0,0,0,20,120,10.62K, 0,0,0,20,155,10.6K, 0,0,20,40,175,10.27K, 0,0,0,80,195,10.13K, 0,0,0,40,265,10.11K, 0,0,20,60,285,10.84K, 0,0,20,120,305,10.24K, 0,0,20,160,315,10.36K, 0,0,0,0,0,9545, 0,0,0,0,445,9415, 0,0,0,0,840,8985, 0,0,0,120,1840,8360, 0,40,0,960,3590,7085, 0,560,0,2060,5050,5870, 0,960,0,2240,5670,5140, 0,1120,40,2800,5795,4500, 0,1560,80,3080,6240,4320, 0,2440,60,3320,6900,3205, 0,3480,40,3940,7075,2350, 0,5000,60,4940,7505,1005, 0,5840,140,5820,7775,0, 0,0,0,0,250,9250, 0,0,0,0,835,8975, 0,0,0,100,1270,8545, 0,80,20,760,2845,7475, 0,400,0,1360,3995,6530, 0,680,20,1840,4095,5990, 0,920,0,1800,4285,5540, 0,1040,0,2200,4865,5460, 0,1720,20,2480,5345,4685, 0,2640,40,3000,5520,3905, 0,3720,140,3800,6090,2925, 0,4360,220,3940,6185,2210, 0,0,0,0,265,9390, 0,0,0,0,725,9430, 0,0,0,80,1200,9240, 0,160,0,680,2590,7845, 0,280,0,1420,3610,6890, 0,760,20,1440,3905,6490, 0,920,20,1560,4225,6290, 0,1320,0,2020,4125,5735, 0,1520,40,2580,4795,4800, 0,2400,80,2920,4660,4590, 0,2720,100,3600,5420,3615, 0,3920,240,4020,5470,2925, 0,0,0,0,140,9535, 0,0,0,0,535,9090, 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0,4840,220,4240,5680,4035, 0,0,0,0,240,12.42K, 0,0,0,0,640,12.22K, 0,0,0,40,950,11.91K, 0,240,20,440,2020,10.78K, 0,640,20,900,2760,10.32K, 0,760,60,1040,2835,10.06K, 0,1080,80,1260,3070,9930, 0,1080,0,1540,3230,9505, 0,1480,80,1960,3390,8865, 0,2080,80,2580,3520,8020, 0,2960,200,2940,3615,7535, 0,3760,220,2760,3950,6405, 0,0,0,0,50,12.76K, 0,0,0,0,155,12.34K, 0,0,0,0,290,12.36K, 0,0,0,160,555,12.57K, 0,160,20,180,900,11.85K, 0,120,0,380,810,11.66K, 0,160,20,500,950,12.17K, 0,200,20,400,995,11.52K, 0,320,40,560,1180,11.88K, 0,400,0,780,990,11.69K, 0,640,60,680,1220,11.17K, 0,840,40,820,1245,11.09K, 0,0,0,0,50,12.41K, 0,0,0,0,65,12.5K, 0,0,0,0,160,12.3K, 0,0,0,120,240,11.87K, 0,40,20,100,535,11.85K, 0,40,20,120,515,11.87K, 0,120,0,200,525,12.1K, 0,160,0,200,515,11.78K, 0,40,0,320,655,11.64K, 0,200,0,420,590,11.48K, 0,400,60,380,610,11.48K, 0,520,20,400,760,11.72K, 0,0,0,0,10,12.45K, 0,0,0,0,25,12.76K, 0,0,0,0,40,12.55K, 0,0,0,0,100,12.57K, 0,0,0,80,130,12.92K, 0,0,0,40,230,13.04K, 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0,4200,320,3820,5460,7485, 40,5680,480,4180,6150,5910, 0,6400,600,4420,6305,4940, 0,0,0,0,280,15.34K, 0,0,0,60,630,15.33K, 0,40,0,200,1170,14.73K, 0,240,0,760,2240,13.85K, 0,880,20,1760,2915,12.68K, 0,1080,60,1600,3190,11.85K, 0,1280,100,1820,3350,11.6K, 0,1480,60,2260,3500,11.49K, 0,2040,220,2360,3670,10.6K, 0,2960,160,2560,3860,9825, 40,4000,240,3120,4295,8475, 0,5360,400,3320,4115,7865, 0,0,0,0,65,15.79K, 0,0,0,20,230,14.81K, 0,0,0,20,350,15.46K, 0,40,0,220,645,14.85K, 0,240,20,420,830,14.56K, 0,240,40,420,955,14.22K, 0,280,20,500,1005,14.64K, 0,280,20,520,1120,14.26K, 0,560,60,600,1150,13.83K, 0,520,20,840,1210,14.05K, 0,1120,60,680,1125,13.56K, 0,1360,140,860,1330,13.09K, 0,0,0,0,65,15.15K, 0,0,0,0,90,15.48K, 0,0,0,80,195,15.59K, 0,0,0,60,415,14.79K, 0,0,20,260,480,15.06K, 0,80,20,220,435,14.76K, 0,80,20,240,640,14.18K, 0,80,0,280,670,15.11K, 0,160,40,380,600,14.65K, 0,320,60,420,645,14.63K, 0,480,40,500,780,14.24K, 0,480,200,640,735,13.94K, 0,0,0,0,10,15.33K, 0,0,0,0,10,15.66K, 0,0,0,0,65,15.51K, 0,0,0,60,85,15.9K, 0,0,0,40,205,15.41K, 0,0,0,40,215,15.22K, 0,0,20,80,225,15.39K, 0,0,0,80,260,15.57K, 0,0,0,60,335,15.5K, 0,0,60,140,400,14.9K, 0,40,40,160,300,14.98K, 0,80,20,240,350,14.67K, 0,0,0,0,0,20.26K, 0,0,0,0,575,19.13K, 0,0,0,200,1675,19.06K, 0,0,0,400,3130,17.95K, 0,960,20,2360,5645,15.06K, 0,2640,60,3880,7160,11.61K, 0,3480,160,4480,7135,11.39K, 40,4760,160,4580,7670,10.02K, 0,5160,220,5560,7665,8490, 0,6760,460,5520,8620,7225, 40,8520,620,6920,8700,5130, 0,12.12K,1140,7500,9500,2075, 0,14.24K,1200,7800,9785,0, 0,0,0,0,430,19.82K, 0,0,0,80,1160,19.38K, 0,40,0,320,2425,18.5K, 0,480,0,1620,4340,16.38K, 0,1960,120,3180,5435,13.73K, 40,2880,300,3440,5525,13.01K, 0,3480,240,3600,5820,12.18K, 40,3880,300,3800,5915,11.4K, 40,5920,400,4600,6465,10.17K, 0,7440,740,4740,6745,8460, 40,8960,1000,5860,7000,6540, 40,11.92K,1180,5880,7390,4245, 0,0,0,20,435,20.28K, 0,0,0,40,1195,18.92K, 0,40,0,300,2025,18.42K, 0,680,0,1380,3860,16.97K, 0,1520,20,2680,4840,14.75K, 0,2200,180,3180,5100,13.47K, 0,3080,200,3420,5110,13.19K, 0,3520,240,3740,5320,12.63K, 40,5080,460,3900,5925,10.69K, 0,6160,560,4880,5900,9510, 80,8240,940,5160,6335,7625, 40,9360,1140,5140,6515,6205, 0,0,0,0,285,19.96K, 0,0,0,80,800,19.52K, 0,40,20,260,1535,18.82K, 0,480,20,1200,2520,17.81K, 0,1480,140,1780,3485,15.77K, 0,1760,100,2180,3590,15.67K, 0,1920,160,2540,3365,15K, 0,2400,160,2600,3805,15.17K, 0,3680,260,2560,4070,13.44K, 120,4320,360,3400,4035,12.53K, 0,5960,620,3920,4450,11.64K, 80,7440,1040,3860,4295,10.61K, 0,0,0,0,110,19.67K, 0,0,0,20,300,19.7K, 0,40,0,60,440,19.45K, 0,80,0,220,785,19.47K, 0,320,0,480,1065,18.82K, 0,360,20,660,1005,18K, 0,360,0,640,1100,18.23K, 0,560,40,840,1040,18.52K, 0,680,180,820,1330,17.98K, 0,1200,60,880,1285,17.7K, 0,1160,260,960,1350,17.11K, 0,1720,340,980,1450,16.89K, 0,0,0,0,70,20.24K, 0,0,0,0,110,19.63K, 0,0,0,100,220,20.38K, 0,80,20,160,375,19.77K, 0,120,40,200,575,19.45K, 0,240,60,260,600,19.35K, 0,160,0,300,645,19.23K, 0,360,80,360,595,19.02K, 0,360,60,400,740,18.9K, 0,400,100,520,770,18.92K, 40,680,120,700,775,18.55K, 0,880,220,440,945,17.96K, 0,0,0,0,35,20.05K, 0,0,0,0,10,19.85K, 0,0,0,20,95,20.33K, 0,0,0,40,185,19.39K, 0,0,40,40,195,19.56K, 0,0,0,80,265,19.79K, 0,40,20,160,270,19.02K, 0,0,40,120,295,19.27K, 0,40,20,160,270,19.5K, 40,40,40,180,390,19.36K, 40,0,20,240,410,19.38K, 40,80,100,260,395,19.2K, 0,0,0,0,0,27.08K, 0,0,0,20,785,26.58K, 0,80,0,140,2065,25.72K, 0,120,0,520,3795,24.92K, 0,1800,60,3120,6460,20.5K, 0,4680,280,5280,7845,15.9K, 40,6240,420,5040,8045,14.88K, 40,7240,600,5880,8710,13.08K, 0,8080,540,6560,8700,12.37K, 40,11K,880,6800,9225,9670, 120,14K,1300,8020,9310,6755, 200,18.96K,2140,8380,9995,2770, 440,21.72K,2660,9200,10.64K,0, 0,0,0,0,605,26.87K, 0,0,0,100,1455,25.85K, 0,40,0,600,2855,24.59K, 0,1280,20,2180,4855,22.15K, 0,3800,240,4120,6120,18.29K, 40,5080,340,4380,6260,17.85K, 0,6120,380,4720,6205,16.48K, 0,6720,440,5000,6595,15.19K, 40,8600,860,5640,7185,13.67K, 0,11.24K,1400,5940,7140,11.36K, 200,13.68K,1820,6700,7815,8050, 240,16.32K,2400,6920,7885,6375, 0,0,0,0,570,26.51K, 0,0,20,120,1465,26.5K, 0,200,0,440,2500,25.22K, 0,1360,60,2000,4450,22.44K, 0,3120,200,3500,5315,19.51K, 0,3720,380,4280,5660,19.06K, 40,5120,400,3880,5705,17.38K, 40,5840,620,4520,6060,16.97K, 40,8040,920,4620,6545,14.72K, 40,10K,1020,5260,6550,13.39K, 160,12.24K,1820,6340,6695,10.32K, 240,14.76K,1820,6300,7185,8090, 0,0,0,40,445,26.16K, 0,0,0,120,905,27.22K, 0,160,0,420,1685,24.54K, 0,1040,40,1500,2955,23.75K, 0,2200,120,2620,3845,22.04K, 0,3280,260,2540,3825,21.43K, 40,3360,200,3160,3965,20.23K, 0,3960,380,3340,4155,19.83K, 40,5520,660,3540,4455,18.18K, 40,7000,720,3740,4515,17.05K, 80,9160,1440,4400,4675,15.42K, 280,10.12K,1720,5040,4530,14.12K, 0,0,0,0,125,26.89K, 0,0,0,20,320,26.85K, 0,0,0,100,570,26.32K, 0,120,20,420,955,26K, 0,640,80,840,1015,25.41K, 0,560,60,940,1095,25.4K, 0,800,40,680,1210,24.85K, 0,960,160,860,1155,25.08K, 40,1320,140,980,1270,24.17K, 40,1840,200,1020,1395,24.05K, 40,1720,340,1440,1325,23.25K, 0,2480,580,1180,1505,23.89K, 0,0,0,0,60,27.35K, 0,0,0,0,140,26.17K, 0,0,0,60,275,26.75K, 0,80,20,180,595,26.18K, 0,320,40,360,585,26.46K, 0,320,80,440,660,26.46K, 0,440,140,400,755,25.6K, 0,480,60,540,660,25.41K, 40,520,160,700,730,26.01K, 0,680,180,660,830,25.06K, 40,1080,280,580,880,25.4K, 40,1160,420,660,905,25.13K, 0,0,0,0,40,27.26K, 0,0,0,0,70,27.19K, 0,0,0,0,105,27K, 0,0,0,40,215,26.24K, 0,40,40,60,250,26.98K, 0,40,0,140,375,26.68K, 0,40,80,140,300,26.48K, 0,0,100,180,355,26.92K, 0,160,80,200,305,26.43K, 0,120,120,220,350,26.1K, 40,200,240,280,375,26.49K, 40,160,200,220,490,25.25K, 0,0,0,0,0,36.99K, 0,0,0,60,1170,35.92K, 0,0,0,380,2645,35.81K, 0,400,0,1220,4700,33.18K, 0,3240,180,4560,7465,27.84K, 0,8200,600,6340,9140,22.41K, 80,9760,1080,7140,9185,20.17K, 40,12.24K,980,7580,9505,18.21K, 160,13.76K,1440,7780,9815,16.78K, 80,17.96K,2020,8280,10.34K,13.19K, 280,21.12K,2940,9680,10.52K,9215, 600,28.32K,4360,9660,10.6K,3725, 760,32.36K,5260,9840,11.11K,0, 0,0,0,40,800,36.62K, 0,0,0,260,1945,35.98K, 0,440,0,960,3380,34.39K, 0,2600,140,3160,5590,29.73K, 0,6640,520,5080,6955,25.56K, 0,8400,720,5200,7040,24.39K, 40,9360,980,6440,7020,22.46K, 120,11.44K,1140,6080,7445,21.38K, 80,14K,1640,6500,7680,19.01K, 200,16.96K,2460,7000,7950,15.38K, 440,20.56K,4060,7660,8125,11.65K, 920,23.44K,4180,7920,8015,8840, 0,0,0,20,835,36.77K, 0,0,0,200,1865,36.14K, 0,120,0,940,3210,34.81K, 0,2320,200,3000,5025,30.61K, 0,6160,500,4060,6215,27.09K, 40,7440,700,4200,6420,25.35K, 0,7880,860,5320,6555,24.45K, 40,9960,840,5100,6895,23.59K, 160,12.48K,1540,6060,6890,20.33K, 280,15.28K,2440,6180,7200,18.16K, 880,17.92K,3120,7360,7510,14.33K, 760,20.76K,4020,7480,7305,11.3K, 0,0,0,60,570,36.51K, 0,0,0,200,1345,36.14K, 0,320,0,660,2310,35.45K, 0,1560,80,1960,3540,33.03K, 40,4440,500,3160,4090,29.51K, 40,5360,420,3620,3985,29.74K, 0,6080,700,3940,4275,28.28K, 80,6720,900,4220,4535,27.08K, 120,8840,1440,4260,4470,25.32K, 200,10.52K,1740,4680,4645,24.2K, 320,13.4K,2360,4380,5055,21.01K, 520,14.76K,3260,5440,4905,19.65K, 0,0,0,20,150,37.06K, 0,0,0,40,380,37.61K, 0,40,20,160,605,36.48K, 0,440,40,500,1040,36.09K, 0,1080,120,860,1260,35.64K, 0,1200,260,1080,1190,34.81K, 0,1600,240,1080,1105,34.98K, 0,1560,300,1120,1420,35.04K, 80,2120,460,960,1500,33.92K, 120,2640,760,1160,1540,33.24K, 120,3000,920,1400,1515,32.96K, 360,3600,940,1260,1555,32.27K, 0,0,0,0,80,36.84K, 0,0,0,0,180,37.19K, 0,40,0,20,395,37.12K, 0,240,60,260,660,36.83K, 0,640,40,360,755,36.02K, 40,680,180,540,825,35.98K, 0,640,140,500,815,35.54K, 0,800,280,420,845,36.17K, 0,1120,260,560,830,34.99K, 0,1160,520,640,985,34.62K, 80,1360,660,700,950,33.92K, 200,1440,620,740,1005,34.41K, 0,0,0,0,60,37.07K, 0,0,0,0,105,37.08K, 0,0,0,60,130,37.63K, 0,0,20,80,300,36.91K, 0,40,20,200,350,37.27K, 0,80,120,240,385,35.65K, 0,80,80,240,360,36.66K, 0,120,140,180,405,36.45K, 0,40,120,320,395,36.67K, 40,280,220,200,385,36.22K, 80,160,220,360,510,36.35K, 80,200,320,420,470,35.55K, 0,0,0,0,0,51.32K, 0,0,0,80,1520,51.08K, 0,40,0,660,3545,48.71K, 0,1000,0,2080,5600,46.29K, 0,6480,400,5580,8335,37.8K, 40,13.84K,1420,8060,9635,30.57K, 80,16.28K,1880,8360,10.29K,28.11K, 280,18.72K,2520,9000,9940,25.88K, 320,21.96K,3120,9460,10.89K,22.79K, 640,26.92K,4200,10.02K,10.86K,17.12K, 1080,31.76K,6000,10.84K,11.18K,12.83K, 2200,38.52K,8080,11.04K,11.94K,5235, 2320,43.4K,9760,12K,12.25K,0, 0,0,0,80,1095,50.47K, 0,40,0,480,2650,48.53K, 0,720,40,1820,3995,47.09K, 0,5360,320,4320,6225,40.98K, 120,11.84K,1020,5600,7545,36.01K, 200,14K,1440,5740,7500,33.63K, 240,15.92K,1880,6300,7825,31.7K, 200,16.8K,2020,7320,7760,29.73K, 600,21.16K,3880,7160,8150,25.5K, 920,24.96K,4400,8280,8075,21.76K, 1560,29.28K,6880,8200,8510,15.64K, 2040,32.6K,7920,8280,8820,11.98K, 0,0,0,40,1150,51.16K, 0,40,0,420,2420,48.65K, 0,560,20,1260,3700,47.61K, 0,4120,340,4140,5855,41.65K, 80,9800,1460,5260,6760,36.81K, 120,11.64K,1680,5560,6925,35.81K, 120,13.48K,1740,6060,7060,33.72K, 240,14.8K,2280,6040,7115,31.44K, 520,19K,3700,6640,7000,28.66K, 920,21.68K,4760,7000,7650,24.32K, 1760,26.48K,6320,7220,7905,19.3K, 2120,28.68K,8460,8080,7795,15.25K, 0,0,0,40,765,51.11K, 0,40,0,380,1675,50.03K, 0,640,40,920,2865,48.89K, 0,3160,240,2560,3830,45.11K, 200,7320,920,3980,4230,41.74K, 80,9280,1080,4040,4370,40.32K, 240,10.04K,1440,3900,4755,38.2K, 280,11.16K,2000,4220,4860,37.9K, 400,13.36K,2640,4720,4845,35.26K, 560,15.68K,3420,5040,4985,32.75K, 1320,17.88K,4380,5600,5155,28.99K, 1760,20.76K,5560,5380,5140,26.95K, 0,0,0,20,275,50.53K, 0,0,20,160,480,51.17K, 0,160,0,180,720,50.46K, 0,720,100,680,1085,49.01K, 0,1640,340,900,1500,48.08K, 0,1960,420,1200,1400,47.36K, 40,2360,540,940,1480,46.95K, 120,2640,580,1440,1630,47.1K, 80,2920,860,1360,1675,46.73K, 240,3640,1160,1400,1550,46.54K, 480,4080,1620,1380,1740,45.54K, 560,4880,2120,1460,1775,43.26K, 0,0,0,0,75,50.66K, 0,0,0,20,245,50.83K, 0,40,0,100,505,51.95K, 0,440,80,380,740,51.05K, 40,720,260,580,895,50.22K, 0,1040,360,480,895,49.16K, 80,1000,300,600,930,49.34K, 120,1320,380,660,900,49.77K, 200,1520,440,620,1065,47.46K, 400,1640,720,1000,970,48.04K, 320,1960,1040,820,975,47.81K, 280,2240,900,780,985,47.52K, 0,0,0,0,35,51.85K, 0,0,0,40,85,51.79K, 0,0,0,20,215,51.06K, 0,0,60,120,345,50.42K, 0,80,100,200,390,50.05K, 0,240,120,280,335,50.57K, 40,240,80,320,420,49.74K, 40,120,200,260,520,50.48K, 40,240,200,260,435,50.4K, 120,280,400,400,445,50.11K, 160,320,320,320,535,49.8K, 240,240,380,520,555,49.25K, 0,0,0,0,0,67.06K, 0,40,0,260,2040,67.72K, 0,200,0,1200,4530,64.97K, 0,1880,100,3160,6530,61.52K, 0,10.8K,860,6840,9465,49.7K, 320,21.68K,3000,9440,10.53K,40.51K, 360,25.32K,3380,9540,10.96K,36.88K, 600,28.2K,4360,10.26K,10.62K,34.18K, 1160,31K,5400,10.12K,11.37K,30.54K, 1560,37.68K,7180,11.62K,11.31K,24.11K, 2480,43.24K,9100,11.84K,11.97K,16.88K, 3800,51.32K,13.58K,12.66K,12.08K,6640, 6240,57.16K,15.88K,12.82K,12.17K,0, 0,0,0,200,1530,66.26K, 0,320,0,760,3320,64.99K, 0,1600,40,2340,4875,61.55K, 40,8360,660,5440,7020,54.44K, 200,16.72K,2280,7060,7835,46.43K, 320,20.2K,3120,6540,7935,43.97K, 720,22.76K,3560,7440,8250,41.17K, 720,24.68K,4280,7520,8330,39.34K, 1440,29.32K,5860,8120,8745,34.02K, 2560,33.68K,7620,8880,9045,28.41K, 3600,38.84K,10.38K,9280,8625,20.89K, 5600,42.8K,12.32K,9300,8940,15.78K, 0,0,0,120,1475,65.87K, 0,200,0,820,3050,65.99K, 0,1200,60,2180,4195,62.44K, 80,7440,860,5060,6350,55.72K, 280,14.68K,2220,6300,7005,48.77K, 560,17.32K,3000,6660,7190,47.08K, 520,20.32K,3340,6700,7210,44.24K, 760,21.52K,4560,7120,7290,41.97K, 1240,26.4K,5160,7440,8090,36.63K, 1680,29.4K,7320,7960,7820,32.6K, 3480,33.92K,10.34K,7900,8215,25.53K, 4680,37.24K,11.68K,8500,8095,20.37K, 0,0,0,100,1100,67K, 0,200,0,560,2170,66.75K, 0,1240,100,1520,3130,64.93K, 80,5480,520,3560,3960,59.55K, 320,10.56K,1820,4280,4580,54.69K, 280,12.48K,2120,4940,4930,52.32K, 600,14.2K,2700,4700,4750,51.45K, 680,15.6K,2800,4480,5030,49.93K, 840,18.56K,4100,5400,5025,46.17K, 1720,20.76K,5260,5240,5235,43.44K, 2600,24.12K,7020,5200,5345,37.49K, 3080,26.36K,8880,5600,5225,34.63K, 0,0,0,20,330,66.16K, 0,0,0,200,595,66.15K, 0,200,20,460,830,66.13K, 0,1520,180,740,1375,64.49K, 80,2120,620,1380,1400,63.76K, 120,2960,660,1160,1705,63.61K, 240,2840,1120,1300,1685,62.94K, 80,3480,1340,1560,1550,61.66K, 320,4080,1560,1500,1730,61.63K, 480,4280,2120,1660,1785,61.02K, 840,4800,2860,1580,1665,59.93K, 1400,5840,2820,1680,1640,58.72K, 0,0,0,0,120,67.91K, 0,0,0,40,385,67.26K, 0,0,0,280,480,66.54K, 0,480,160,520,855,66.93K, 40,1240,380,500,805,66.14K, 80,1480,500,520,955,64.72K, 40,1680,440,600,905,65.17K, 40,1640,640,740,1015,65.28K, 240,1920,1000,900,1100,64.1K, 360,2240,1020,800,1050,63.83K, 480,2680,1600,700,1150,63.74K, 1000,2600,1600,940,1155,62.35K, 0,0,0,0,45,67.02K, 0,0,0,60,110,67.53K, 0,0,0,20,245,67.49K, 0,80,80,120,310,66.88K, 0,120,160,320,415,66.59K, 80,280,160,240,440,66.33K, 40,200,280,360,475,66.23K, 0,360,260,220,540,66.21K, 80,240,400,400,565,66.3K, 160,400,540,380,525,65.44K, 360,520,500,440,490,66.21K, 400,400,620,520,460,66.35K, 0,0,0,0,0,84.33K, 0,40,0,240,2735,83.05K, 0,600,0,1800,5095,79.71K, 0,3120,120,3840,7405,76.58K, 200,15.68K,1600,8040,10.25K,63.19K, 680,28.88K,4580,10.38K,11.01K,50.42K, 1240,34.48K,5780,10.18K,11.32K,46.76K, 1240,37.6K,7440,11.04K,11.74K,42.22K, 1920,42.28K,7880,11.94K,11.81K,38.02K, 2920,48.2K,10.96K,12.58K,12.12K,30.03K, 5320,55.12K,12.76K,12.9K,12.13K,21.31K, 7640,64.64K,18.78K,13.66K,12.47K,8450, 11.16K,71.48K,20.8K,13.44K,12.23K,0, 0,0,0,200,1925,83.75K, 0,480,20,1400,3880,82.64K, 0,2560,140,3080,5365,78.61K, 0,12K,1460,6580,7595,67.83K, 680,22.76K,3800,7920,7945,59.24K, 1160,26.52K,4400,8120,8320,56.34K, 1320,29.12K,5700,8020,8620,53.25K, 1600,31.4K,7360,8400,8835,49.51K, 2760,37.4K,8780,8560,8795,42.41K, 4240,43.84K,10.84K,9260,8650,36.74K, 6680,48.96K,14.7K,9000,9155,26.42K, 8200,52.6K,16.8K,9940,8960,19.52K, 0,40,0,100,1875,83.93K, 0,360,20,1120,3470,82.34K, 0,1960,140,2800,4890,79.62K, 40,10.92K,1140,5760,6890,71.06K, 920,20.64K,3500,6560,7515,61.18K, 1080,21.92K,4080,7700,7735,58.02K, 1200,24.8K,5440,7900,7980,54.29K, 1400,28.2K,6200,7500,7600,52.27K, 2320,32.12K,8680,8800,7865,46.32K, 4000,36.8K,10.38K,8580,8240,41.03K, 6440,42.12K,14.88K,8360,8305,32.02K, 7840,45.64K,16.9K,8620,8810,26.36K, 0,80,0,180,1280,84.24K, 0,280,0,1020,2495,83.13K, 40,2040,80,1920,3605,80.21K, 120,7360,940,4280,4385,75.53K, 760,13.44K,2740,4900,4760,68.55K, 640,16K,3880,5320,4830,66.4K, 1040,17.8K,4400,5060,4945,65.52K, 1360,19.6K,4980,5240,5380,62.77K, 2000,23.28K,5900,5500,5185,58.02K, 3040,24.92K,8020,5880,5530,54.56K, 4840,29.32K,10.42K,5680,5395,48K, 6520,30.48K,12.78K,6560,5475,44.41K, 0,0,0,0,385,84.46K, 0,40,0,240,685,85.54K, 0,400,20,540,990,83.34K, 40,1680,340,1340,1480,82.05K, 160,2960,1020,1280,1580,81.8K, 280,3560,1260,1340,1675,79.98K, 240,3880,1180,1460,1755,79.7K, 600,4120,1680,1460,1685,78.28K, 1000,5080,2300,1600,1740,76.64K, 1280,5120,2580,1660,1825,76.92K, 1840,5080,3300,2060,1855,74.07K, 2000,6320,3600,1720,1890,72.95K, 0,0,0,20,210,85.88K, 0,80,20,80,390,85.33K, 0,40,0,300,635,84.94K, 0,1000,200,380,895,84.22K, 40,1360,600,760,915,81.73K, 80,1760,620,840,1030,82.46K, 280,1840,760,820,990,80.75K, 360,2000,1080,880,1090,81.65K, 400,2040,1360,1140,1200,80.45K, 560,2480,1780,1060,1145,80.52K, 1520,2760,1860,980,1175,79.22K, 1200,2920,1880,1180,1125,79.95K, 0,0,0,0,80,84.9K, 0,0,0,40,180,84.79K, 0,0,0,40,260,84.97K, 0,80,100,220,465,84.25K, 0,200,200,300,535,84.43K, 80,320,340,340,450,83.71K, 160,440,220,380,440,83.83K, 280,520,280,380,435,83.35K, 280,520,440,380,425,84.1K, 400,560,600,440,485,83.82K, 480,600,580,480,510,82.36K, 880,400,680,520,525,82.21K, 0,0,0,0,0,100.5K, 0,40,0,560,3095,99.43K, 0,880,0,2280,5615,96.06K, 0,4800,140,5040,7865,91.22K, 240,21.4K,2420,8860,10.07K,76.11K, 1240,36.8K,6640,11.16K,11.68K,60.79K, 1640,41.72K,7960,12.14K,11.91K,56.19K, 2240,46.68K,10K,12.2K,11.74K,50.55K, 3480,50.44K,11.46K,12.52K,11.59K,45.34K, 5200,58.64K,14.66K,12.38K,12.18K,35.89K, 7880,65.2K,17.74K,13.6K,12.36K,25.59K, 12.84K,76K,22.88K,14.04K,12.7K,10.04K, 16.24K,81.32K,26.44K,14.6K,13.06K,0, 0,80,20,420,2220,102.2K, 0,880,20,1780,4470,98.29K, 0,3840,180,3820,6090,94.58K, 160,16.28K,1960,6840,8005,81.85K, 960,28.92K,4760,8500,8425,70.27K, 1640,32.92K,6300,8140,8755,67.23K, 2160,35.36K,7500,9040,8910,62.99K, 2720,39.28K,9080,8840,8635,58.87K, 4600,44.36K,11.94K,9260,8860,51.39K, 6600,50.48K,14.56K,9800,8635,42.25K, 10.56K,56.68K,18.58K,10.34K,9035,30.98K, 13.28K,60.96K,20.86K,10.32K,9335,23.36K, 0,80,0,340,2020,100.2K, 0,520,40,1560,3970,98.45K, 0,3600,180,3400,5095,93.93K, 160,14.76K,1820,6540,6975,83.28K, 1120,24.84K,4880,7860,7565,73.19K, 1760,28K,5520,8140,7550,70K, 1760,30.8K,7760,7860,7965,66.73K, 2600,33.88K,8240,8160,8135,61.37K, 4520,38.96K,10.76K,8280,8320,56K, 6880,43.52K,13.66K,9080,7705,48.64K, 10.72K,48.4K,16.74K,9140,8365,38.34K, 13.12K,52K,19.98K,9700,8615,31.24K, 0,40,0,240,1500,100.1K, 0,560,40,1200,2770,98.97K, 40,2400,100,2680,3830,96.94K, 40,10.16K,1580,4200,4520,89.48K, 880,17.8K,3560,5180,4655,81.21K, 1080,19.64K,4420,5460,5145,81.46K, 1560,21.72K,5880,5740,5270,77.54K, 2200,23.76K,6280,5380,5160,75.13K, 3360,27.12K,8940,5220,5385,69.44K, 4640,29.8K,10.74K,5900,5320,64.52K, 7600,33.72K,12.68K,6440,5480,57.69K, 9640,35.4K,14.78K,6020,5425,53.1K, 0,0,0,60,435,101.3K, 0,120,20,340,750,101.2K, 0,680,80,580,1070,99.62K, 0,2600,680,1020,1480,98.18K, 280,3680,1480,1420,1655,96.1K, 360,4440,1780,1260,1560,95.25K, 520,5000,1860,1320,1740,96.22K, 640,5120,2320,1660,1555,94.48K, 960,5720,3160,1920,1685,93.21K, 1760,5920,3760,1740,1785,90.84K, 2560,6080,3980,1840,1755,89.06K, 3760,6400,4400,2060,1875,88.07K, 0,0,0,60,235,101.4K, 0,40,0,140,410,101.3K, 0,280,40,260,670,100.4K, 40,1200,380,620,935,100.6K, 320,1840,840,740,1005,98.32K, 160,2040,1120,940,940,96.07K, 440,2000,1160,840,1015,98.61K, 480,2320,1200,880,1170,96.8K, 920,2360,1580,1100,1155,96.36K, 1120,2560,2020,1120,1170,96.02K, 1760,3080,2320,820,1130,94.41K, 2240,3200,2440,1200,1245,94.17K, 0,0,0,0,65,101K, 0,0,0,20,165,102.8K, 0,0,20,120,265,101.7K, 80,160,200,360,330,100.1K, 0,400,300,420,475,100.8K, 80,160,380,500,510,100.9K, 200,400,580,500,550,100.2K, 280,320,560,600,550,99.68K, 360,200,600,660,450,100.1K, 560,720,720,360,555,99.7K, 920,440,840,580,600,99.17K, 1280,640,820,460,545,98.65K, 0,0,0,0,0,115.8K, 0,80,0,760,3365,113.2K, 0,1440,20,3000,6320,110K, 0,6120,300,6000,8335,103.2K, 280,26.24K,3040,9700,10.95K,87.53K, 1800,43.6K,8120,12.3K,11.49K,68.91K, 2920,49.68K,9840,11.58K,12.13K,64.03K, 3680,54.12K,11.98K,11.7K,12.56K,58.7K, 5000,58.6K,13.62K,12.9K,12.49K,52.17K, 7560,67K,17.8K,13.54K,12.29K,40.91K, 10.48K,76.28K,21.86K,12.94K,12.77K,28.82K, 17.04K,86.08K,27.2K,14.6K,13.13K,11.3K, 22.4K,91.44K,30.6K,13.9K,12.76K,0, 0,40,0,580,2550,114.4K, 0,1040,0,2340,4550,111.1K, 40,4840,280,4300,6345,106.4K, 400,19.56K,2720,7720,8115,94.72K, 1880,33.48K,6660,8460,8685,80.88K, 2240,37K,8220,8880,8680,76.4K, 3280,41.36K,9520,9100,8865,71.77K, 3720,44.04K,11.18K,9520,8650,68.19K, 5840,51.28K,14.08K,9480,9280,57.69K, 9160,57.6K,18.4K,9780,8955,48.77K, 14.76K,64.68K,22.16K,10.22K,9130,35.88K, 19.48K,66.52K,25.76K,10.28K,9500,26.65K, 0,80,0,560,2355,114.5K, 0,1160,0,2020,4180,112.9K, 0,4760,320,3860,5925,108.1K, 520,18.08K,2840,6400,7425,95.75K, 1960,28.6K,6040,7940,7815,84.02K, 1960,33.08K,7840,8020,8245,79.23K, 2720,35.52K,9080,7980,8205,75.27K, 4680,38.2K,11.08K,8980,8255,70.98K, 5760,44K,13.94K,8860,8605,63.58K, 8920,48K,16.84K,9420,8290,56.79K, 14.36K,53.72K,20.5K,9760,8290,43.76K, 17.68K,56.48K,22.96K,9680,8625,36.1K, 0,120,0,200,1690,114.5K, 0,760,20,1540,2735,112.1K, 0,3440,220,2580,3900,109.6K, 360,12.92K,1740,4560,4540,102.6K, 1200,21.2K,4640,5520,4815,93.5K, 1600,22.64K,5540,5660,5190,90.73K, 2080,25.32K,6880,5340,4885,87.21K, 2880,26.64K,7720,5740,5260,86.53K, 5040,30.08K,10.22K,5700,5325,80.97K, 6440,33.16K,12.32K,6400,5440,73.83K, 9360,36.32K,15.86K,6420,5330,65.08K, 13.12K,37.52K,17.02K,6240,5435,60.37K, 0,40,0,140,420,116.5K, 0,120,0,440,795,114.1K, 0,880,100,640,1270,116K, 40,2920,640,1200,1620,111.9K, 600,4600,1640,1440,1645,109.6K, 360,5320,2220,1460,1660,108.7K, 720,5320,2220,1640,1805,107.6K, 1000,5560,2960,1580,1830,107.5K, 1800,5960,3200,1880,1815,105.9K, 2320,6680,4160,1720,1910,104.2K, 3960,6640,4740,1960,1915,102.3K, 4960,7400,5140,1740,1750,101K, 0,0,0,40,280,116.7K, 0,40,0,140,485,116.7K, 0,360,0,360,625,116K, 40,1360,440,660,1035,113.8K, 480,2120,960,940,1025,112.1K, 200,2120,1200,940,1090,111.2K, 520,2120,1680,920,1030,111.7K, 520,2680,1340,980,1000,111.2K, 1080,2800,2000,940,1085,110.7K, 1480,2800,2240,1000,1110,109.6K, 2520,3120,2640,1120,1075,108.6K, 2920,3080,2820,1200,1205,107.3K, 0,0,0,0,125,116.5K, 0,0,0,0,210,116.2K, 0,0,0,120,380,115.9K, 40,240,200,360,315,115.5K, 240,400,400,300,545,114.3K, 200,440,500,300,545,114.2K, 280,440,780,420,600,114.6K, 400,640,480,420,530,113.5K, 520,640,600,340,570,114K, 720,480,680,540,515,112.4K, 880,640,780,480,560,113.3K, 1520,440,1040,520,670,113K, 0,0,0,0,0,128.4K, 0,160,0,660,3840,125.7K, 0,1680,40,3300,6720,119.9K, 40,7800,400,6300,8660,113.9K, 560,29.96K,3560,10.24K,11.02K,95.59K, 2520,48K,9480,12.56K,12.16K,77.47K, 3440,55.28K,11.56K,12.76K,11.82K,69.82K, 4640,60.32K,13.78K,13K,12.07K,63.74K, 6120,64.28K,15.28K,13.32K,12.21K,58.22K, 10K,74K,20.08K,13.2K,12.69K,44.87K, 14.12K,81.72K,24.42K,14.36K,13.08K,31.16K, 21.2K,93.08K,30.48K,14.1K,13.28K,13.14K, 26.64K,98.68K,34.48K,14.9K,13.04K,0, 0,120,0,540,2930,124.5K, 0,1360,20,2340,4935,123K, 0,5320,360,4860,6555,118.3K, 360,22.48K,3400,7500,8225,101.3K, 1800,36.4K,7840,9000,8820,88.05K, 2760,41.68K,9080,8520,8960,81.84K, 4160,45.64K,10.72K,9280,8730,77.44K, 5240,48.84K,12.96K,9100,9030,73.27K, 7760,56.64K,17.36K,9020,8970,62.8K, 11.72K,60.52K,19.2K,10.24K,9170,53.39K, 17.68K,68.36K,25.08K,10.32K,9205,39.22K, 21.96K,72.36K,27.58K,10K,9995,28.32K, 0,80,0,640,2830,124.3K, 0,1360,20,2300,4830,122.7K, 40,5720,260,4040,5930,118K, 240,21.16K,3380,6780,7250,106K, 2200,31.04K,7480,8680,8020,91.81K, 2760,35.8K,8700,8480,8180,88K, 3880,38.36K,10.7K,8820,8435,84.55K, 4880,42.4K,12.04K,8420,8225,78.67K, 8520,46.08K,15.88K,9440,8315,69.58K, 11.68K,51.2K,18.64K,8920,8565,60.95K, 17.88K,58.04K,22.18K,9520,8400,47.05K, 22.12K,59.6K,25.46K,9780,8605,39.28K, 0,40,0,360,1755,125.3K, 0,1040,60,1720,2940,124.1K, 0,4120,340,3060,3980,121K, 520,14.36K,2080,4720,4790,110.6K, 1440,23.04K,5520,5040,5035,102.8K, 2240,25.24K,7080,5220,5415,97.95K, 2480,27.48K,8320,5740,5075,95.33K, 3840,28.6K,9060,5840,5085,93.6K, 5360,32.28K,10.78K,6100,5530,88.14K, 8440,35.8K,13.74K,6140,5370,81.36K, 12.6K,38.4K,16.68K,6280,5610,72.12K, 15.76K,40.12K,18.98K,6180,5340,66.68K, 0,0,0,160,555,127.1K, 0,200,20,460,805,125.9K, 0,960,180,960,1160,126.3K, 200,3240,780,1480,1555,123.7K, 560,4920,2240,1400,1570,120.6K, 680,5440,2540,1720,1825,119K, 800,6080,2740,1400,1585,118.6K, 1240,6000,2960,1620,1735,117.2K, 2280,6360,3700,1900,1790,115.3K, 3080,6800,4540,1780,1790,115.1K, 4720,7160,5180,1740,1745,111.3K, 5360,7760,5640,1860,1730,109.8K, 0,0,0,80,310,125.6K, 0,40,20,180,595,127.3K, 0,240,100,460,835,125.8K, 120,1680,400,720,985,125.6K, 400,2440,1020,860,1090,124.6K, 400,2440,1480,860,1185,121.1K, 880,2560,1400,1160,920,120.9K, 640,2960,1900,1020,1105,122.9K, 1440,2880,1860,980,1065,120.3K, 2080,3080,2280,980,1115,120.4K, 2280,3480,2900,1140,1150,118.6K, 3400,3520,2880,980,1150,117.6K, 0,0,0,0,105,125.3K, 0,0,0,40,210,125.2K, 0,0,20,200,345,126.1K, 0,200,220,420,480,127.1K, 160,320,340,380,510,125.9K, 320,280,600,440,505,124.3K, 400,600,540,400,490,124.9K, 480,400,660,500,560,125.2K, 600,640,720,480,535,125K, 880,720,760,540,480,124.5K, 1440,600,980,540,585,123.9K, 1640,480,900,580,665,124K, 0,0,0,0,0,133.7K, 0,200,0,840,4040,131.1K, 0,2400,80,3840,6545,126.2K, 40,9520,520,6720,9000,120.2K, 680,33.88K,4520,10.22K,11.06K,99.92K, 2680,53.04K,10.6K,12.14K,11.92K,79.56K, 4400,59.68K,12.54K,13.04K,12.37K,73.73K, 5400,64.04K,15.14K,13.04K,12.47K,66.79K, 6280,69.44K,17.4K,12.92K,12.53K,59.72K, 11.28K,78.08K,21.9K,13.92K,13.21K,46.46K, 15.88K,86.8K,25.72K,14.24K,12.76K,33.27K, 22.96K,98.2K,32.68K,14.68K,12.9K,13.39K, 29.44K,103.7K,35.94K,15.12K,13.13K,0, 0,160,0,720,2875,132.7K, 0,1480,140,2840,5095,127.9K, 40,6520,400,5500,6420,122.7K, 440,24.68K,3620,7820,8240,108.1K, 2840,40.2K,8340,8860,8785,92.81K, 3640,44.12K,11.1K,9160,8955,88.21K, 4920,48.68K,12.66K,9480,8850,82.98K, 6200,51.12K,14.46K,9780,8980,77.73K, 8840,58.12K,18.18K,10.1K,9570,67.53K, 12.12K,65.12K,20.32K,9300,9040,56.09K, 20.32K,70.72K,25.36K,10.26K,9320,40.82K, 24.92K,75.28K,29.22K,10.18K,9520,31.91K, 0,160,0,600,2890,130.4K, 0,1760,0,2520,4520,128.8K, 40,6400,260,4380,5900,125K, 440,21.64K,3740,7020,8030,110.2K, 2240,34.56K,8060,8420,7925,96.5K, 3400,38.28K,9180,8960,7920,92.12K, 4400,41.52K,12.02K,8420,7940,86.76K, 6120,44.28K,13.48K,8660,8305,82.01K, 8960,49K,16.28K,9220,8560,73.69K, 12.56K,52.88K,19.76K,10.36K,8255,63.85K, 18.96K,58.68K,24.5K,9660,8465,50.01K, 24.56K,62.56K,26.88K,9580,8420,41.61K, 0,120,20,380,1950,132.9K, 0,1080,40,1680,3205,128.6K, 0,4600,300,3300,4185,129.1K, 400,15.24K,2740,4920,4720,117.1K, 2160,24.84K,5700,5100,5040,108.1K, 2440,26.76K,7260,5660,5355,104.2K, 3200,29K,8820,5740,5045,101.1K, 4000,30.08K,9920,6040,5315,99.06K, 6840,34.88K,12.44K,5940,5335,92.02K, 9280,35.6K,15K,6700,5305,84.73K, 14.4K,38.96K,17.76K,6660,5540,75.82K, 18.56K,39.4K,20.24K,6600,5615,70.25K, 0,0,0,120,540,133.3K, 0,280,0,420,935,133.4K, 0,1200,120,800,1370,132K, 160,3560,940,1480,1545,130.8K, 480,4840,2320,1460,1695,127.4K, 1160,5000,2540,1680,1910,125.1K, 1160,5800,3040,1780,1730,125.1K, 1720,6200,3080,2040,1695,123.1K, 2320,6840,3920,1900,1605,121.8K, 3720,6800,4380,1660,1830,120K, 5280,7320,4840,1920,1830,116.7K, 6520,7960,5440,1820,1910,115.7K, 0,0,0,80,290,134.9K, 0,80,0,280,455,132.3K, 0,640,40,480,620,131.7K, 160,1840,500,820,885,131.9K, 320,2520,1140,840,1175,130.9K, 600,2400,1220,1200,1160,128.7K, 680,2640,1580,960,1220,129K, 920,2920,1920,980,1175,127.8K, 1560,3040,2320,1040,1105,126.5K, 1880,2880,2480,1120,1260,126.1K, 3480,3520,2580,1080,1145,123.9K, 3800,3760,2960,1200,1070,123.5K, 0,0,0,0,50,133.8K, 0,0,0,80,230,134.7K, 0,120,40,140,395,133.4K, 40,240,400,340,470,133.6K, 240,360,660,400,445,131.3K, 520,400,420,480,520,132.5K, 440,640,520,320,530,132.2K, 400,440,840,480,580,130.8K, 840,640,760,360,575,132K, 1040,640,740,600,580,129.7K, 1280,600,980,620,565,130.3K, 1800,840,1000,420,600,129.6K, 0,0,0,0,0,135.2K, 0,360,0,1060,4010,133.3K, 0,2120,20,3720,7360,130.7K, 0,10.32K,340,6700,9185,121.6K, 400,34.64K,4800,10.56K,11.08K,101.8K, 2800,54.76K,10.18K,12.48K,12.01K,81.08K, 4520,59.92K,13.14K,13.14K,12.44K,73.84K, 5840,66.48K,15.9K,12.7K,12.22K,67.84K, 7520,70.32K,17.18K,14.1K,12.47K,62.02K, 11.76K,80.4K,22.56K,13.72K,12.7K,48.32K, 16.12K,88.68K,26.54K,15.12K,13.05K,33.88K, 23.44K,100.8K,33K,15.06K,12.96K,13.59K, 31.4K,104.2K,36.4K,15.34K,13.48K,0, 0,240,0,720,2875,132.1K, 0,1840,120,2500,5115,131.2K, 40,7600,480,5360,6540,125.7K, 720,25.88K,3520,7940,8245,108.9K, 3000,41.6K,8800,8920,9085,93.02K, 3480,44.96K,11.12K,9300,8640,88.51K, 4800,49.56K,12.36K,9640,8920,84.89K, 5680,52.32K,15K,10.22K,9180,79.68K, 10.16K,60.6K,17.14K,9940,9245,67.63K, 12.84K,65.32K,21.56K,10.6K,9415,56.84K, 19.96K,73.48K,26.94K,10.06K,9405,42.26K, 26.56K,76.92K,29.08K,10.6K,9895,31.99K, 0,160,0,600,2700,133.2K, 0,1760,20,2500,4610,130.8K, 0,6680,480,4700,6095,127K, 800,21.8K,3640,7300,7765,111.4K, 2680,36.28K,8460,7940,7985,98.08K, 3480,38.96K,9460,8760,8270,94.19K, 4440,42K,11.8K,8560,8200,88.75K, 6200,45.6K,13.76K,8700,8070,83.56K, 9560,50.84K,17.44K,9560,8340,74.45K, 13K,54.44K,20.4K,9480,8610,65.27K, 20.32K,59.76K,24K,9960,8525,50.66K, 25.52K,62.64K,26.76K,10.42K,8900,41.77K, 0,160,0,460,1805,134.8K, 0,1120,80,1620,3240,133.2K, 0,4680,400,3760,4120,129.4K, 480,16.4K,2720,4900,4825,119.5K, 2120,25.48K,6100,5380,5035,109.9K, 2320,27.04K,7620,5560,5595,107.4K, 3680,29.64K,9160,5440,5280,102.7K, 4720,31.08K,9880,5820,5415,100K, 6600,33.92K,12.92K,6280,5455,93.35K, 10.08K,36.2K,14.9K,6040,5435,86.81K, 12.96K,40K,19.3K,6500,5600,77.11K, 17.48K,41K,20.42K,6940,5455,69.24K, 0,0,0,160,540,134.8K, 0,360,20,360,935,135.9K, 40,1120,120,960,1300,133.6K, 120,3560,940,1480,1655,131.4K, 640,5000,2040,1760,1545,128K, 1400,4960,2180,1700,1755,127.8K, 1520,6080,2960,1740,1800,127.4K, 1360,6280,3560,1680,1710,125.9K, 2840,6880,3900,1700,1715,124.3K, 3680,7120,4480,1760,1700,122.5K, 5200,7200,5120,2180,1740,119K, 6960,7800,5380,1780,2000,117.5K, 0,0,0,20,370,136.4K, 0,40,40,260,575,135.1K, 0,520,60,580,675,132.8K, 80,1800,520,760,1000,132.3K, 400,2440,1260,980,975,131.2K, 600,2480,1480,920,1140,132.4K, 760,2560,1600,980,1165,129K, 1160,3040,1740,980,1110,129.5K, 1560,3120,2360,1020,1055,128.9K, 2560,3000,2420,1180,1200,128.3K, 3040,3280,2940,1120,1165,126.4K, 4040,3920,3100,900,1155,126.2K, 0,0,40,0,50,134.9K, 0,0,0,80,250,135.3K, 0,0,40,180,360,136.2K, 40,240,300,400,455,134.7K, 280,360,520,440,565,133.8K, 320,480,580,400,580,133.9K, 560,440,520,380,660,133.6K, 480,480,760,360,560,132K, 960,480,800,480,525,133.9K, 1160,680,880,440,545,134.2K, 1360,600,1060,560,575,130.9K, 2000,520,1020,620,535,132K, 0,0,0,0,0,134.7K, 0,160,20,920,3955,129.4K, 0,2400,40,3520,6835,126K, 0,9920,500,6460,9255,120.8K, 680,33.72K,4540,10.64K,11.04K,99.35K, 2720,54K,10.64K,12.26K,12.42K,80.56K, 4240,60.96K,12.3K,12.52K,12.03K,72.48K, 5680,66.44K,14.08K,13.68K,12.54K,66.52K, 7240,71.48K,15.98K,13.92K,12.54K,59.38K, 11.28K,80.88K,20.72K,14.34K,12.7K,46.18K, 15.28K,89.28K,26.02K,14.18K,13.35K,33.09K, 24.32K,100.5K,32.32K,14.62K,13.41K,13.36K, 30.28K,106.1K,36.4K,14.56K,13.24K,0, 0,240,0,720,2990,131.7K, 0,2600,60,2480,5155,127.6K, 0,7440,380,5060,6820,123.9K, 400,26.52K,3820,7760,7990,107K, 2520,41.2K,8800,9060,8690,92.46K, 3800,45.52K,10.06K,9320,8920,86.43K, 4520,49.84K,11.96K,9600,8975,82K, 5680,54K,14.18K,9580,9245,75.46K, 9400,59.68K,17.34K,9780,9170,67.22K, 12.6K,66.68K,21.12K,9440,9085,56.59K, 19.88K,73.32K,25.68K,10.18K,9170,41.48K, 24.56K,77.08K,28.68K,10.54K,9580,31.44K, 0,160,0,740,2950,130.4K, 0,1560,60,2260,4610,126.9K, 40,7080,260,4500,5990,124K, 560,21.8K,3460,7500,7430,110.3K, 2680,35.96K,8220,8560,7665,95.84K, 3600,38.36K,9720,8860,8330,90.6K, 4800,42.6K,11.16K,8720,8070,86.41K, 5480,45.04K,13.3K,9320,8385,82.52K, 8440,50.96K,16.06K,9380,8470,73.61K, 12.44K,54.88K,19.24K,9740,8545,64.09K, 19.56K,60.84K,23.92K,9920,8860,50.9K, 23.88K,63.44K,26.38K,10.06K,8810,41.22K, 0,200,0,440,1865,131.1K, 0,1480,60,1740,3065,130.1K, 40,4160,400,3620,4225,126K, 440,16.08K,2200,4980,5075,117K, 1880,25.6K,5740,5400,4955,107.5K, 2520,27.48K,7660,5500,5320,104.6K, 3480,29K,8560,5800,5225,101.3K, 4160,30.84K,9980,6080,5320,97.62K, 7000,34.2K,11.84K,5520,5380,91.76K, 9840,36K,14.8K,6320,5550,84.6K, 13.64K,40.28K,17.78K,6200,5605,75.2K, 17.44K,41.6K,20.44K,6440,5630,69.23K, 0,40,0,120,525,132.7K, 0,280,0,340,1010,130.5K, 0,1000,120,1040,1320,130.7K, 200,3800,700,1340,1610,127.8K, 600,5080,2220,1560,1745,126K, 640,5400,2660,1700,1700,124.4K, 1000,5960,2900,1840,1735,123.9K, 1440,6000,3080,1720,1615,122.5K, 2160,6360,3960,2080,1720,121.6K, 3080,7640,4420,1800,1740,119.5K, 5000,7360,5460,1900,1810,117.8K, 6240,7440,5780,1880,1850,114K, 0,0,0,60,340,133.5K, 0,120,0,240,485,131.2K, 0,520,140,460,765,131.3K, 80,1640,560,840,995,131.2K, 600,2280,1120,1020,970,129.7K, 520,2560,1260,900,1165,129.1K, 680,2760,1720,1100,1075,128.3K, 960,2800,1880,1000,1180,127.8K, 1240,3000,2040,1120,1165,126.7K, 1960,2960,2480,1200,1065,125.2K, 3000,3440,2740,1120,1065,124.8K, 3840,3440,3080,1260,1165,123.1K, 0,0,0,0,115,133.5K, 0,0,0,20,220,132K, 0,0,0,100,400,132.9K, 160,280,200,420,450,131.3K, 160,400,580,540,595,131.2K, 320,520,560,360,560,131.7K, 400,600,540,420,525,132.2K, 600,400,680,420,625,131.8K, 600,480,840,480,550,130.3K, 1120,560,800,480,610,129.8K, 1160,680,1180,520,595,128.9K, 2280,560,960,540,560,129.2K, 0,0,0,0,0,126.1K, 0,120,0,880,3810,122K, 0,2240,60,3560,6735,120.4K, 0,9520,340,6940,8510,111.9K, 640,33.2K,3560,10.42K,11.02K,93.86K, 2480,52.4K,9860,12.38K,12.15K,75.84K, 3240,58.48K,12.28K,12.38K,11.93K,69.92K, 4120,66.6K,13.1K,13.2K,12.24K,62.32K, 6480,69.88K,15.64K,13.04K,12.52K,56.58K, 8720,79.84K,20.46K,13.12K,13.01K,44.17K, 12.36K,89.2K,24.6K,14.08K,12.67K,31.63K, 20.6K,97.72K,29.18K,14.1K,13.65K,12.49K, 25.16K,105.5K,34.66K,15.8K,12.97K,0, 0,120,0,720,3035,124.8K, 0,1520,20,2800,5330,119.6K, 0,6640,280,4900,6530,116K, 560,24.84K,2800,7640,8345,101.9K, 2000,40.4K,7240,8640,8905,87.07K, 3480,44.96K,10K,8720,8910,81.34K, 3680,47.28K,10.7K,9960,9085,76.52K, 5360,50.88K,12.16K,10K,9245,70.98K, 8400,58.08K,16K,9700,9170,62.31K, 11.88K,64.44K,19.5K,10.18K,9325,53.4K, 17.32K,71.2K,24.02K,10.02K,9620,38.28K, 21.28K,74.04K,26.62K,11.08K,9870,28.98K, 0,200,0,620,2895,124.5K, 0,1400,20,2360,4490,119.3K, 80,6200,320,4260,6055,116.2K, 480,21.84K,2700,7460,7100,102.5K, 2200,34.52K,7320,8260,7970,91.45K, 2880,38.8K,8540,8320,8255,87.79K, 3240,39.84K,9920,9000,8515,81.69K, 4960,44.64K,12.6K,9020,8360,77.12K, 7880,49.72K,14.96K,9160,8460,68.35K, 11.4K,53.48K,18.72K,9300,8790,60.36K, 17.48K,59.24K,22.74K,10.4K,8615,46.53K, 21.68K,62.4K,24.04K,9680,8625,38.14K, 0,160,0,420,1800,124.4K, 0,1160,60,1440,3410,122.3K, 0,4560,280,3420,4125,120.2K, 360,16.24K,2520,4440,4740,109.4K, 1440,24.4K,5100,5900,4855,100.5K, 1880,26.6K,6640,5920,5265,98.26K, 2680,29.24K,7560,5480,4755,94.59K, 4080,30.36K,8820,5780,5495,91.95K, 5840,32.68K,11.32K,6380,5600,86.28K, 7680,35.84K,13.7K,6160,5510,80K, 12.28K,39.48K,16.6K,6660,5590,71.28K, 15.16K,39.92K,18.92K,6600,5690,64.33K, 0,0,0,80,565,125.1K, 0,80,20,600,925,125.1K, 0,1320,140,820,1185,124.8K, 120,3480,800,1240,1675,122.4K, 560,5280,2180,1420,1595,119.2K, 840,5360,2220,1800,1755,118.5K, 1040,5600,2760,1720,1655,117.8K, 1120,5920,3240,1680,1850,116.2K, 2080,6360,3460,2020,1910,113.9K, 2920,7160,4520,1820,1850,113.3K, 4520,7520,5280,1700,1740,110K, 5840,7520,4980,1840,1890,107.8K, 0,0,0,60,275,125.6K, 0,40,0,280,555,124K, 40,600,80,480,725,125.4K, 40,1400,600,760,995,123.3K, 440,2360,1100,960,1000,121.5K, 480,2480,1420,980,1100,121.6K, 560,2800,1760,900,1115,120.5K, 720,2560,1780,960,1100,121K, 1320,2880,1960,1140,1150,119.7K, 1840,2840,2180,1120,1120,117.7K, 2600,3440,2820,1080,1250,116.3K, 3480,3360,3120,1160,1125,115.5K, 0,0,0,0,165,126.5K, 0,0,0,0,215,125.2K, 0,40,40,160,240,125K, 80,400,200,280,430,123.6K, 280,400,420,380,535,123.8K, 240,280,720,520,550,121.5K, 400,440,560,540,520,122.8K, 440,320,620,560,595,123.8K, 720,400,760,460,600,122.1K, 960,360,860,440,630,121.9K, 1480,640,940,540,535,121.3K, 1760,560,980,500,520,122.1K, 0,0,0,0,0,112.8K, 0,240,0,740,3310,110.5K, 0,2200,100,3180,6250,106K, 0,7920,340,6460,8380,101.1K, 400,28.84K,2940,10.04K,11.18K,84.86K, 1600,48.76K,8140,12.8K,11.88K,66.81K, 2280,55.28K,10.32K,11.98K,11.94K,61.01K, 3360,62.24K,10.76K,12.84K,11.98K,56.68K, 4560,64.96K,12.88K,13.02K,12.22K,50.33K, 7200,73.84K,16.82K,13.4K,12.84K,39.14K, 10.36K,84.36K,20.52K,14.08K,12.6K,27.99K, 16.36K,92.72K,25.32K,14.12K,12.84K,11.52K, 20.72K,101.2K,29.94K,14.42K,12.62K,0, 0,120,0,500,2910,112.2K, 0,1320,0,2460,4825,109K, 0,5960,180,4820,6415,105.3K, 320,23.04K,2540,7560,7985,91.17K, 1440,38K,6380,8620,8580,76.71K, 2040,42.16K,7560,9160,8360,73.54K, 2880,45.76K,9620,8920,8560,70.42K, 3920,47.68K,10.86K,9200,9150,64.66K, 6320,55.16K,13.66K,9580,9080,56.27K, 7640,60.96K,17K,10.06K,9340,47.74K, 13.72K,66.48K,21.42K,10.52K,9335,34.49K, 16.88K,72.36K,24.92K,10.48K,9405,25.97K, 0,200,0,560,2575,109.2K, 0,1240,20,2620,4290,109.5K, 80,5280,180,4280,5785,105.4K, 400,20.44K,2540,6760,7575,93.96K, 1360,32.28K,6220,8260,7625,81.15K, 2040,36.52K,7540,8620,7770,77.31K, 3120,38.56K,8500,8580,7915,72.79K, 3400,42.12K,9940,8700,8275,69.44K, 6000,46.92K,13.4K,9120,8355,61.51K, 8560,50.56K,16.76K,9820,8365,54.26K, 14.44K,56.64K,18.74K,10.36K,8570,41.17K, 16.36K,60K,22.9K,9240,8745,34.46K, 0,120,0,360,1730,113.1K, 0,1000,20,1480,3240,109.6K, 0,3880,240,3480,4140,105.5K, 400,13.92K,1820,5140,4735,99.02K, 1040,22.64K,4580,5300,4885,90.66K, 1640,24.72K,5540,5700,5245,88.09K, 1760,27.32K,7000,5620,5100,85.26K, 2640,29K,7160,5780,5505,82.2K, 3760,32K,9920,6140,5350,77.09K, 6440,34.44K,11.68K,6160,5815,72.21K, 9320,39.64K,13.86K,6040,5255,63.42K, 12.8K,39.36K,16.7K,6160,5650,57.71K, 0,0,0,80,440,111.8K, 0,280,0,400,855,111.4K, 0,1000,60,560,1265,112.1K, 80,3240,560,1200,1625,108.3K, 320,4600,1760,1660,1830,106.7K, 720,4880,1920,1920,1650,106.7K, 480,5440,2420,1660,1645,104.9K, 1120,5640,3020,1600,1645,104.2K, 1400,6320,3100,1740,1770,102.8K, 2360,6920,3360,1800,1700,100.4K, 3200,7160,4640,1880,1835,98.21K, 4440,7840,4660,1960,1585,96.95K, 0,0,0,80,240,113K, 0,80,0,220,540,111.3K, 0,480,20,400,675,110.6K, 0,1400,460,620,1040,109.4K, 80,2120,1040,960,1090,107.6K, 320,2440,1100,840,1040,109.1K, 320,2680,1080,900,1125,110.3K, 520,2280,1520,1020,1145,108.6K, 1040,2840,1920,980,1075,108.5K, 1200,2920,2300,1180,1130,106.7K, 2000,3000,2760,1360,1195,104.9K, 3120,3240,2540,1280,1150,104.1K, 0,0,0,0,85,112.6K, 0,0,0,0,180,112.9K, 0,0,40,80,370,111.6K, 40,200,180,360,425,110.5K, 200,320,400,520,430,109.9K, 240,320,460,360,535,110.3K, 320,360,460,500,555,111.3K, 400,400,500,460,630,109.2K, 560,400,720,520,535,110.7K, 760,520,740,460,545,109.3K, 1120,600,700,580,585,109.7K, 1280,520,1000,500,595,107.2K, 0,0,0,0,0,97.52K, 0,120,0,580,3170,94.08K, 0,1600,20,2760,6065,90.77K, 0,6920,200,5600,8135,86.64K, 200,24.88K,1880,9960,10.69K,72.04K, 1040,45K,5780,11.06K,11.69K,56.72K, 1680,49.08K,7540,11.92K,12.1K,53.37K, 2040,55.88K,8480,11.96K,12.18K,47.89K, 3000,58.72K,10.64K,12.08K,12.12K,42.9K, 4520,68.36K,12.48K,13.24K,12.57K,33.92K, 6600,76.24K,16.1K,13.56K,12.56K,24.21K, 10.24K,86.08K,21.08K,13.86K,12.53K,9550, 13.76K,92.56K,24.38K,14.52K,12.8K,0, 0,40,0,400,2445,94.7K, 0,1120,20,2100,4555,94.67K, 0,4640,200,4060,6485,88.98K, 240,20.16K,1660,7640,7725,76.51K, 840,33.48K,4320,8480,8505,66.89K, 1120,36.04K,6120,9320,8645,63.89K, 1960,40.48K,6960,9220,8195,58.7K, 2680,43.56K,8380,9120,8765,55.21K, 3960,49.08K,10.72K,9760,9205,48.12K, 5560,55.88K,13.98K,9400,8930,41.4K, 9280,63.2K,16.82K,9880,9100,29.84K, 12.28K,66.8K,20.54K,10.14K,9435,22.38K, 0,80,0,500,2320,95.62K, 0,880,20,1900,4225,91.79K, 0,4520,140,3760,5630,90.17K, 160,17.56K,1800,6340,7290,79.62K, 680,28.56K,4980,8220,7810,69.51K, 1480,32.88K,5480,8160,7860,66.31K, 1920,34.92K,6740,8200,7945,62.31K, 2680,38.32K,8400,8620,7645,59.87K, 3840,42.6K,10.08K,9000,8285,52.61K, 5400,47.92K,12.82K,9020,8185,45.56K, 8200,52.36K,16.66K,9420,8665,36.22K, 11.8K,54.92K,18.66K,10.14K,8270,29.38K, 0,0,0,360,1550,95.53K, 0,920,0,1380,2810,93.97K, 0,3400,100,2600,3935,91.4K, 80,12.2K,1140,4760,4690,83.75K, 880,20.36K,3340,4980,4995,77.69K, 1080,21.84K,4540,5540,5000,75.36K, 1200,24.92K,5360,5440,4940,73.25K, 1800,27.28K,5660,5540,5120,70.68K, 2880,30K,7760,5780,5275,66.52K, 4920,31.68K,9460,6220,5355,61.9K, 7200,36.44K,11.6K,6180,5440,55.26K, 8480,36.28K,14.24K,6580,5490,49.39K, 0,0,0,60,435,95.72K, 0,0,0,460,775,96.83K, 0,640,20,800,1110,94.2K, 0,2760,600,1120,1540,93.06K, 240,4280,1340,1480,1675,90.49K, 360,4800,1300,1540,1675,89.01K, 160,5000,1760,1640,1815,89.94K, 880,5280,2080,1620,1625,88.95K, 1200,5520,2720,1920,1780,87.57K, 1640,6280,2880,1920,1700,86.6K, 2760,6840,3860,1760,1820,83.92K, 3280,7200,3980,2000,1595,83.02K, 0,0,0,60,200,95.89K, 0,80,0,280,395,95.54K, 0,280,20,360,715,95.23K, 40,1160,320,680,975,93.46K, 160,2000,820,840,1055,93K, 160,2080,880,1020,1115,92.21K, 240,2560,800,1000,1040,92.54K, 360,2280,1120,960,1150,92.44K, 560,2560,1920,920,1070,90.46K, 840,3120,1980,1000,1175,89.8K, 1480,3200,2260,900,1170,89.78K, 1640,3200,2220,1240,1035,89.17K, 0,0,0,0,110,95.75K, 0,0,0,0,185,95.87K, 0,80,20,140,265,95.49K, 80,320,100,240,425,94.77K, 80,360,260,340,490,96.19K, 160,280,400,480,475,95.95K, 240,320,400,420,500,95.38K, 280,400,480,460,535,94.98K, 520,320,580,480,535,93.43K, 320,680,780,400,515,94.05K, 600,600,980,520,625,93.79K, 880,760,840,440,505,94.34K, 0,0,0,0,0,78.81K, 0,40,0,380,2975,76.61K, 0,960,20,2100,5460,74.92K, 0,4760,100,5100,7640,70.18K, 120,21.48K,1300,8660,10.2K,59.91K, 560,36.92K,4060,10.72K,11.24K,47.16K, 960,41K,5220,11.46K,11.69K,44.02K, 1160,47.76K,5780,11.14K,11.88K,40.3K, 1560,51.08K,6820,11.9K,11.68K,36.43K, 2240,59.4K,9800,12.5K,12.24K,27.47K, 4200,66.36K,12.66K,13.1K,12.35K,20.08K, 6080,75.92K,16.16K,14.12K,12.55K,7940, 8360,83.28K,18.36K,13.68K,12.9K,0, 0,40,0,260,2270,77.86K, 0,680,20,1920,4130,75.57K, 0,3640,40,3440,5755,72.7K, 120,16.08K,1080,6980,7150,63.42K, 640,28.08K,2940,8000,8070,54.51K, 640,31.8K,4220,8240,8945,51.9K, 1160,34.88K,5500,8580,8415,48.04K, 1680,37.56K,6100,9060,8650,45.24K, 2360,43.56K,8400,9360,8680,39.65K, 3480,49.76K,9740,9280,9075,32.94K, 5320,56.64K,13.58K,9640,9495,24.29K, 7760,59.56K,15.2K,10.32K,9150,18.21K, 0,0,0,360,2120,77.92K, 0,720,20,1420,3690,77.2K, 40,3560,80,3220,5145,74.73K, 80,14.04K,860,6260,7030,65.31K, 320,25.84K,2800,7020,7755,56.9K, 680,27.64K,4080,7900,7590,53.84K, 840,30.56K,4540,8000,8090,50.64K, 1480,32K,6060,8420,8175,49.83K, 2680,36.88K,8180,8760,8255,43.83K, 2640,42.08K,10.02K,9080,8175,37.67K, 5680,47.8K,12.98K,9380,8080,29.57K, 6440,51.8K,14.58K,9420,8395,24.27K, 0,80,0,140,1395,78.28K, 0,600,0,1120,2525,78.46K, 40,2160,20,2120,3700,75.74K, 200,10.32K,740,4100,4465,69.2K, 280,16.76K,2460,4900,4970,63.35K, 760,19K,2640,5400,5050,62.3K, 920,22.36K,3300,5440,4725,59.79K, 1280,23.12K,4240,5380,5190,58.04K, 1960,26.56K,5260,5540,5255,53.85K, 2560,28.68K,7100,5860,5295,50.27K, 4120,33.68K,8820,5680,5235,45.3K, 4840,34.44K,10.82K,5640,5505,41.25K, 0,0,0,100,365,79.63K, 0,120,0,280,675,78.15K, 0,840,0,600,1060,77.99K, 0,2280,280,1100,1365,76.27K, 40,3640,740,1420,1630,74.96K, 200,4200,1060,1340,1780,74.29K, 200,4640,1180,1760,1640,74.41K, 320,4760,1460,1640,1605,74.31K, 480,5320,2020,1420,1870,72.86K, 800,5680,2580,1800,1925,71.56K, 1400,6360,3120,1700,1730,70.59K, 1840,6760,3320,1840,1715,68.01K, 0,0,0,0,165,79.85K, 0,40,0,200,390,79.72K, 0,80,20,440,605,77.76K, 0,960,120,580,860,77.26K, 120,1920,580,760,980,76.9K, 120,2000,620,840,1000,76.38K, 160,2240,740,880,1130,75.62K, 160,2320,960,720,1155,75.64K, 280,2400,1320,740,1110,75.3K, 680,2760,1440,1000,1090,75.34K, 1000,2960,1760,1000,1230,73.47K, 1520,3280,1980,1100,1065,73.6K, 0,0,0,0,100,79.13K, 0,0,0,20,170,78K, 0,0,0,140,200,78.78K, 40,80,60,360,415,78.96K, 80,160,300,340,435,79.19K, 80,320,320,320,455,78.03K, 120,280,300,380,415,77.48K, 160,320,360,360,545,78.19K, 240,360,500,360,535,77.44K, 200,480,560,460,575,77.12K, 440,440,580,480,580,77.48K, 520,600,700,400,600,75.91K, 0,0,0,0,0,64.21K, 0,80,0,180,2485,62.49K, 0,520,20,1580,4955,59.77K, 0,3240,100,4020,6875,55.88K, 120,15.52K,620,8300,9495,47.48K, 160,29.44K,2540,10.08K,10.68K,37.48K, 360,32.68K,3340,11K,11.24K,35.34K, 640,38.48K,3800,11.28K,11.08K,31.88K, 1000,41.92K,4000,10.98K,11.65K,28.46K, 1360,49.8K,6780,12.16K,11.32K,22.62K, 1440,55.8K,8680,12.34K,11.84K,15.44K, 3240,66.2K,11.64K,13.28K,12.33K,6250, 4360,71.96K,14.6K,12.98K,12.39K,0, 0,0,0,300,1965,63.17K, 0,520,0,1540,3670,60.69K, 0,1920,60,3140,5010,59.02K, 40,12K,680,5740,7390,51.11K, 80,22.24K,2120,7240,8295,43.47K, 360,25.44K,2780,7800,8320,41.69K, 440,28.52K,2960,8380,8035,39.84K, 640,30.84K,3880,8580,8310,36.97K, 1080,36.68K,5580,8500,8530,31.65K, 1840,41.36K,7280,9300,8580,26.26K, 3160,48.84K,9640,9300,8890,19.49K, 3960,53.16K,11.32K,9240,9115,14.74K, 0,0,0,180,1755,63.36K, 0,360,0,1060,3580,61.89K, 0,2080,80,2820,4845,58.89K, 40,10.76K,720,5840,6260,52.45K, 280,19.64K,1860,7140,7445,45.65K, 400,22.32K,2500,7440,7625,43.52K, 360,24.8K,2980,7020,7785,42.17K, 680,27.2K,3560,7720,7540,39.17K, 1000,32.92K,5220,7720,7990,34.76K, 1360,35.72K,7040,8220,8350,30.23K, 3040,41.6K,8920,8380,8140,24.09K, 3880,46.12K,10.92K,8600,8130,19.43K, 0,0,0,220,1260,62.86K, 0,440,0,840,2155,62.49K, 0,2040,20,1600,3280,60.66K, 80,7680,560,4280,4145,55.01K, 240,13K,1540,4620,4825,51.06K, 160,15.08K,2020,5000,5060,49.17K, 400,18.24K,2380,4720,4700,48.3K, 320,18.56K,3100,5240,5285,45.88K, 920,22.16K,3660,5440,5045,43.31K, 1280,24.72K,4840,5660,5370,40.42K, 2040,29.2K,7400,5420,5385,36.56K, 2800,31.2K,7720,5960,5140,33.92K, 0,0,0,40,340,63.24K, 0,80,0,180,605,62.96K, 0,280,0,560,1025,62.49K, 0,1880,100,900,1400,60.81K, 40,3120,380,1140,1670,59.7K, 80,3360,780,1380,1585,59.38K, 40,3880,1040,1620,1490,59.39K, 120,4080,1200,1420,1695,59.21K, 200,4680,1360,1500,1655,57.47K, 200,5440,1660,1740,1685,57.42K, 520,6080,2280,1600,1675,54.78K, 640,6240,2680,1720,1750,54.69K, 0,0,0,0,160,62.37K, 0,0,0,80,310,64.57K, 0,80,0,240,620,62.5K, 0,760,160,520,950,62.93K, 0,1480,280,700,1030,62.59K, 40,1480,320,760,900,61.05K, 120,1760,460,880,1060,61.43K, 40,1840,660,840,1120,61.33K, 200,2040,980,860,1075,60.49K, 280,2480,940,940,1030,60.14K, 560,2480,1360,1120,1095,59.7K, 640,2680,1620,1120,1045,58.43K, 0,0,0,0,60,63.98K, 0,0,0,20,175,62.48K, 0,0,20,40,250,63.48K, 0,80,100,140,425,62.43K, 40,160,100,220,490,62.7K, 0,280,220,280,495,62.64K, 40,200,200,400,450,63.06K, 80,360,240,260,460,63.12K, 160,280,400,440,480,62.28K, 200,400,440,460,505,62.62K, 200,520,520,300,585,61.88K, 280,640,660,360,505,61.61K, 0,0,0,0,0,50.77K, 0,0,0,100,2210,51.2K, 0,160,20,1140,4435,49.02K, 0,2280,20,2740,6530,46.89K, 40,11.48K,380,7120,9055,38.92K, 80,23.36K,1680,9700,9865,31.54K, 160,25.96K,1760,9640,10.62K,28.84K, 240,31.08K,2700,10.34K,10.34K,24.98K, 400,33.36K,3000,10.76K,11.31K,23.14K, 760,40.8K,4400,10.92K,11.07K,17.67K, 840,45.52K,6140,11.86K,11.87K,12.88K, 2360,55.36K,8140,12K,11.98K,5280, 2440,60.32K,9940,12.18K,12.18K,0, 0,0,20,120,1605,50.43K, 0,320,0,920,3280,48.59K, 0,1600,40,2400,4640,47.07K, 0,8960,320,5400,6440,41.82K, 40,16.88K,1500,6960,7670,35.86K, 120,20.44K,1660,6960,7945,34.35K, 160,22.8K,2200,6880,7980,31.36K, 480,24.92K,2120,8000,8165,29.18K, 760,29.76K,3420,7880,8605,26.45K, 1040,34.16K,4480,8920,8400,21.98K, 1960,41.4K,6800,8960,8650,15.97K, 2080,45.56K,8420,8680,8770,11.35K, 0,0,0,100,1330,50.05K, 0,200,0,800,2940,49.26K, 0,1280,40,2240,4305,47.63K, 0,8000,360,4280,6380,42.07K, 160,15K,1140,6540,7155,37.96K, 160,17.64K,1240,6740,7240,35.45K, 240,19.48K,1640,7100,7400,33.96K, 240,21.72K,2860,6980,7415,31.91K, 560,25.76K,3800,7560,7840,28.5K, 1000,29.28K,4680,8200,7920,24.7K, 1560,35.2K,6520,8300,8105,19.1K, 2040,38.96K,7900,8320,8250,15.19K, 0,0,0,200,925,51.44K, 0,240,20,440,2210,50.86K, 0,1120,60,1620,2760,49.07K, 0,5680,280,3660,4035,45.18K, 40,10.52K,1080,4380,4485,41.71K, 40,11.92K,1320,4460,4760,40.74K, 200,13.72K,1300,5320,4495,39.66K, 200,15.56K,1940,4760,5145,37.42K, 360,18.16K,2620,5280,4935,35.81K, 560,21.04K,3240,5120,5095,33.2K, 1440,24.6K,4240,5240,5310,29.08K, 1800,26.64K,5200,5380,5460,27.23K, 0,0,0,0,270,50.91K, 0,0,0,100,545,51.04K, 0,200,0,320,905,50.45K, 0,1240,60,960,1250,48.98K, 0,2240,400,1260,1460,48.85K, 40,2600,440,1240,1590,49.63K, 40,2840,500,1200,1605,48.04K, 40,3520,480,1460,1510,48.48K, 0,3800,960,1520,1720,47.66K, 240,4360,1340,1560,1570,45.63K, 520,5320,1660,1520,1730,45.27K, 520,5600,2120,1580,1635,44.27K, 0,0,0,20,120,51.76K, 0,0,0,100,320,51.46K, 0,40,0,200,470,51.2K, 0,440,0,620,755,50.89K, 0,1280,260,620,775,50.16K, 0,1480,220,580,800,48.69K, 40,1400,240,780,915,48.46K, 160,1600,380,720,960,49.15K, 80,1960,620,720,1080,49.13K, 160,2080,720,840,1105,48.58K, 240,2280,1020,940,1080,48.95K, 440,2360,1280,840,1180,48.76K, 0,0,0,0,30,51.64K, 0,0,0,0,110,51.29K, 0,40,0,20,180,51.54K, 0,40,20,140,355,51.53K, 40,160,80,240,420,51.26K, 40,200,140,300,450,51.38K, 40,160,100,380,415,51.07K, 40,320,180,320,435,51.23K, 40,200,220,400,555,50.74K, 120,320,300,380,470,51.66K, 120,400,360,440,460,50K, 200,520,560,360,480,50.01K, 0,0,0,0,0,44.8K, 0,0,0,60,1695,43.63K, 0,200,0,1100,3750,42.73K, 0,1000,0,2940,5845,39.71K, 0,8480,320,6620,8290,33.77K, 40,17.72K,1100,8760,9690,27.31K, 80,21.08K,1380,8720,10.12K,24.76K, 280,24.12K,1600,9780,10.46K,21.94K, 240,26.92K,2500,9440,11.02K,20.57K, 400,33.64K,3480,10.02K,11.3K,15.43K, 920,37.96K,4540,10.6K,11.3K,10.48K, 1040,46.48K,6860,11.6K,11.74K,4695, 1880,51.48K,7880,11.76K,11.42K,0, 0,0,0,40,1410,44K, 0,120,0,680,3030,42.09K, 0,880,0,1900,4170,40.8K, 0,6400,360,4740,6380,36.39K, 40,13.16K,760,6820,7755,30.66K, 160,16.6K,1180,6300,7500,29.4K, 160,17.28K,1600,7320,8110,27.49K, 160,20.92K,2180,7440,8050,25.63K, 440,24.6K,2880,8300,7800,22.93K, 480,28.84K,3960,8580,8405,19.04K, 1280,33.8K,5520,8700,8860,13.49K, 1360,38.44K,6380,8600,8750,10.61K, 0,0,0,40,1295,44.06K, 0,160,0,560,2625,43.12K, 0,960,20,1840,3910,41.24K, 40,5800,380,4380,5810,37.22K, 40,12.08K,660,5540,6945,31.84K, 40,13.44K,1040,6440,6960,30.85K, 160,16.52K,1340,6360,7340,29.51K, 240,17.64K,1940,6860,7105,27.91K, 520,21.96K,2500,7000,7110,23.9K, 640,26.2K,3400,6920,7410,22.26K, 1200,30.2K,5140,7920,7530,16.66K, 1480,34.52K,5660,8080,7945,13.81K, 0,0,0,80,870,43.65K, 0,120,0,460,1905,44.08K, 0,720,0,1040,2730,42.06K, 0,4200,220,3120,3860,39.19K, 40,8200,620,4000,4460,36.13K, 40,10.08K,780,4240,4675,35.27K, 80,11.04K,1080,4460,4880,33.79K, 80,12.28K,1300,4940,4845,33.36K, 200,15.08K,2040,4720,4815,30.66K, 400,18.08K,2460,5040,5100,29.11K, 1200,21.68K,3640,4840,5035,25.03K, 1120,24.24K,4420,5280,5110,22.87K, 0,0,0,20,250,45K, 0,40,0,40,460,44.94K, 0,40,0,400,745,44.15K, 0,800,80,860,1210,42.57K, 0,1880,260,980,1325,42.14K, 0,2280,300,1060,1470,41.9K, 40,2320,400,1080,1625,41.48K, 0,3040,660,1300,1420,40.99K, 0,3280,700,1360,1565,41.07K, 40,3840,720,1680,1450,39.79K, 440,4400,1320,1520,1725,40.04K, 360,4720,1460,1740,1735,38.03K, 0,0,0,0,100,44.69K, 0,0,0,60,275,44.33K, 0,0,0,180,405,44.78K, 0,400,40,460,690,43.28K, 40,800,200,560,835,43.88K, 40,1080,180,700,830,43.13K, 0,1320,280,660,900,43K, 0,1480,320,720,970,43.19K, 120,1480,440,660,985,42.22K, 160,1720,580,800,1075,42.18K, 240,2000,840,800,940,40.82K, 120,2280,1000,940,980,41.46K, 0,0,0,0,55,44.93K, 0,0,0,0,105,44.54K, 0,0,0,20,185,43.88K, 0,40,0,200,335,44.97K, 0,40,100,260,430,44.5K, 0,120,120,280,425,44.06K, 40,160,40,280,475,43.5K, 40,160,180,280,370,44.25K, 0,240,220,240,510,43.42K, 80,240,200,280,480,43.55K, 80,440,340,360,515,44.25K, 120,240,400,420,580,43.66K, 0,0,0,0,0,42.2K, 0,0,0,60,1590,42.1K, 0,120,0,840,3575,40.89K, 0,1200,20,2140,5485,38.21K, 0,7720,260,5760,8000,31.28K, 40,15.88K,860,7460,9710,25.17K, 0,18.32K,1380,8200,9750,23.61K, 80,21.08K,1500,8620,10.05K,21.56K, 160,23.56K,2080,8960,10.68K,19.29K, 280,29.76K,2820,9640,11.1K,14.65K, 800,33.56K,3760,10.72K,11.41K,10.36K, 720,41.68K,5500,11.52K,11.63K,4055, 1560,46.28K,7220,10.94K,12.11K,0, 0,40,0,80,1340,41.08K, 0,120,0,580,2565,41.63K, 0,600,0,1600,4250,38.33K, 40,5440,180,4240,6410,34.88K, 40,11.68K,740,6060,7035,29.86K, 80,13.64K,1140,6480,7550,27.99K, 80,15.04K,1380,6600,7770,26.09K, 160,17.88K,1860,6880,7825,24.76K, 280,20.76K,2640,7720,8010,22.41K, 480,25.76K,3360,8120,8180,18.17K, 840,31.16K,4620,8240,8350,12.88K, 1360,34.08K,5180,8360,8885,9695, 0,0,0,40,1195,41.75K, 0,80,0,480,2410,41.36K, 0,880,0,1540,3595,39.58K, 0,5040,200,3620,5700,35.12K, 40,10.56K,880,5380,6785,31.71K, 160,12.8K,1100,5540,6655,29.1K, 120,13.96K,1480,6060,7090,27.68K, 80,15.8K,1760,6340,7175,25.92K, 160,19.28K,2400,6940,7095,23.59K, 320,23.36K,3240,7120,7200,20.31K, 880,27.76K,4880,7800,7750,16.22K, 1280,30.64K,5780,8180,8180,12.86K, 0,0,0,0,805,43.3K, 0,120,0,280,1870,40.92K, 0,720,20,900,2520,40.4K, 0,3480,120,3060,3820,37.22K, 0,7160,520,3920,4240,33.58K, 80,8680,580,4020,4640,33.71K, 160,9800,1240,4120,4410,32.14K, 80,11.44K,1200,4320,4995,31.93K, 280,13.16K,1780,4880,5215,28.97K, 480,16.56K,1780,4900,5040,26.78K, 800,18.2K,3100,5520,5070,24.58K, 680,21.84K,3760,5480,5230,22.5K, 0,0,0,0,250,42.67K, 0,0,0,60,435,41.54K, 0,40,0,260,755,41.62K, 0,840,40,860,1025,41.68K, 0,1640,180,1160,1255,40.16K, 0,2000,340,1080,1350,40.48K, 0,1920,260,1380,1300,39.59K, 0,2480,440,1240,1505,40.03K, 40,2720,680,1340,1560,39.06K, 200,3640,900,1520,1485,38.14K, 400,4120,1320,1540,1630,38.03K, 200,4280,1280,1740,1665,37.56K, 0,0,0,0,105,42.13K, 0,0,0,20,250,43.22K, 0,0,0,120,420,41.77K, 0,240,20,420,705,41.73K, 0,840,120,440,880,40.78K, 0,800,200,600,895,41.61K, 0,1080,280,700,905,40.12K, 0,1280,300,720,835,41.11K, 80,1400,360,680,995,39.55K, 80,1720,520,580,995,40.22K, 40,1680,900,760,1010,39.85K, 160,2080,860,940,1000,39.31K, 0,0,0,0,45,42.94K, 0,0,0,0,100,42.63K, 0,0,0,20,190,42.88K, 0,0,40,120,370,42.24K, 0,80,60,160,375,42.11K, 0,120,80,220,405,42.1K, 0,80,80,380,380,42.13K, 40,80,120,300,385,42.17K, 0,160,120,260,615,42.08K, 80,280,140,340,450,41.29K, 120,440,200,320,490,41.57K, 160,360,300,360,535,41.47K, 0,0,0,0,0,43.11K, 0,0,0,60,1690,41.96K, 0,200,0,560,3490,40.68K, 0,760,0,2240,5770,38.72K, 0,6880,300,5840,8190,32.63K, 80,14.88K,900,7620,9755,25.27K, 40,17.32K,1320,8200,10.02K,23.83K, 160,19.64K,1480,8720,10.12K,22.02K, 200,22.48K,2120,9060,10.62K,19.41K, 280,27.96K,3060,10.28K,10.88K,14.74K, 640,33.48K,3900,10.32K,10.96K,10.62K, 840,39.72K,5260,11.16K,11.82K,4240, 1480,44.44K,7320,11.78K,11.25K,0, 0,0,0,40,1365,42.75K, 0,0,0,420,2605,41.61K, 0,600,0,1580,4220,39.76K, 40,4880,220,4640,6230,34.55K, 80,11.04K,860,5820,7375,30.51K, 40,13.56K,1000,6060,7315,28.21K, 120,15.24K,1240,6580,7415,26.48K, 240,17.6K,1780,6820,7435,24.98K, 240,19.96K,2660,7880,7950,21.23K, 560,25.32K,3300,8240,7990,18.53K, 1040,29.96K,4660,8960,8450,12.88K, 1720,33.64K,6280,8380,8385,9660, 0,0,0,40,1290,42.32K, 0,160,0,400,2400,42.3K, 0,840,0,1300,3730,39.82K, 0,4560,180,3880,5490,35.77K, 120,10.28K,740,5360,6470,30.12K, 80,11.28K,840,5420,6845,29.55K, 40,13.16K,1240,6220,7120,28.89K, 160,14.84K,2100,6620,6985,26.55K, 440,18.76K,2020,6680,7180,23.81K, 640,21.56K,3300,7420,7565,20.07K, 840,26.88K,4760,7500,7975,16.25K, 1000,29.24K,5200,7980,8060,13.58K, 0,0,0,40,855,41.72K, 0,120,0,320,1650,41.52K, 0,520,20,980,2380,40.05K, 0,2840,100,2980,4330,38.04K, 40,7400,680,3700,4270,34.93K, 40,8880,820,3820,4420,33.5K, 40,9200,1160,4480,4630,32.28K, 160,10.76K,1140,4480,4715,31.41K, 240,13.08K,1900,5340,4910,29.13K, 480,15.52K,2200,4840,4920,27.11K, 760,19.36K,2960,4980,4895,24.5K, 1200,20.84K,4240,5280,5100,21.45K, 0,0,0,0,250,43.89K, 0,0,0,120,500,42.87K, 0,80,0,260,680,42.64K, 0,640,60,860,1110,41.77K, 0,1600,280,1000,1385,40.4K, 0,1960,280,1160,1410,40.33K, 0,2080,420,980,1570,40.05K, 0,2440,460,1180,1490,40.14K, 40,2720,720,1260,1550,39.62K, 40,3520,1020,1320,1540,38.63K, 160,3960,1260,1400,1750,38.38K, 200,4280,1780,1660,1675,37.21K, 0,0,0,0,120,42.14K, 0,0,0,40,245,42.6K, 0,0,0,120,445,42.6K, 0,280,20,440,675,42.45K, 0,880,60,460,830,42.07K, 0,600,120,540,905,41.92K, 0,1040,240,680,915,42.17K, 40,1040,320,660,855,41.14K, 0,1680,280,760,985,41.15K, 120,1640,500,780,975,40.28K, 80,1840,880,680,955,40.01K, 80,2000,960,860,1105,39.36K, 0,0,0,0,30,43.36K, 0,0,0,20,80,43.65K, 0,40,0,0,210,43.82K, 0,0,20,160,230,43.28K, 0,40,100,260,400,41.67K, 0,200,200,120,405,42.17K, 0,160,60,200,435,42.66K, 40,160,140,220,435,42.87K, 80,280,140,340,375,42.92K, 160,400,220,280,465,42.72K, 0,320,260,420,490,41.55K, 120,480,320,320,490,41.66K, 0,0,0,0,0,44.26K, 0,0,0,80,1780,45.32K, 0,80,0,560,3640,43.05K, 0,920,0,2240,5655,40.11K, 0,7000,260,5780,8635,33.39K, 120,15.8K,960,7820,9550,26.71K, 80,18.04K,1240,8320,9895,24.71K, 120,20.16K,1580,9160,10.22K,21.98K, 160,23.24K,2440,9200,10.32K,20.18K, 480,29.36K,3420,9740,10.69K,15.37K, 840,33.32K,4340,11.1K,11.36K,10.58K, 1040,40.12K,6660,11.16K,11.37K,4355, 1960,45.04K,7700,11.64K,11.84K,0, 0,0,0,60,1245,44.11K, 0,40,0,560,2685,43.08K, 0,520,0,1700,4060,41.32K, 40,4880,220,4660,6000,36.46K, 120,11.16K,880,5940,6950,30.84K, 120,13.64K,1100,6260,7430,29.16K, 160,16.16K,1560,6360,7650,26.69K, 200,17.8K,1920,7520,7450,25.81K, 360,21.56K,2820,7400,7930,22.04K, 560,26.36K,3520,7540,8195,19.71K, 960,30.64K,5400,8400,8670,14.32K, 1360,33.48K,6480,8580,8750,10.42K, 0,0,0,20,1205,44.94K, 0,80,0,380,2545,43.37K, 0,760,40,1440,3700,41.61K, 40,5000,180,4260,5500,37.26K, 0,9880,840,5800,6355,32.32K, 0,11.88K,1200,5900,6715,30.62K, 160,13.32K,1460,6600,6890,29.07K, 160,16.2K,1900,6420,7150,27.28K, 280,18.56K,2620,6980,7150,24.61K, 440,23.12K,3700,7100,7435,21.34K, 1040,27.36K,4940,7940,8005,17.06K, 1680,30.24K,6380,7600,7590,13.81K, 0,0,0,120,805,44.25K, 0,40,0,340,1650,44.27K, 0,320,0,960,2585,43.37K, 0,3680,240,2840,3865,39.5K, 120,7280,560,3940,4570,36.19K, 40,8640,820,4080,4395,35.62K, 80,9440,940,4700,4570,33.96K, 280,11K,1320,4700,4640,32.97K, 400,14K,1780,4760,5005,30.75K, 320,15.72K,2960,5040,4970,28.73K, 640,20.32K,3740,4920,5130,25.16K, 840,21K,4880,5340,5165,23.43K, 0,0,0,0,270,43.73K, 0,40,0,40,545,43.83K, 0,120,0,220,695,44.72K, 0,680,60,780,1100,42.73K, 0,1760,220,820,1420,42.18K, 0,2000,280,1260,1425,42.51K, 0,2400,440,940,1400,42.34K, 0,2880,320,1240,1425,41.49K, 80,3000,680,1480,1475,41.18K, 200,3600,860,1240,1650,39.73K, 200,4240,1320,1520,1565,39.65K, 240,4800,1600,1360,1690,38.33K, 0,0,0,0,100,44.8K, 0,0,0,40,305,44.9K, 0,0,20,120,425,43.95K, 0,400,0,520,515,43.63K, 40,720,120,520,875,44K, 0,880,180,500,790,44.04K, 0,1120,220,620,870,43.4K, 80,1160,280,620,910,42.38K, 120,1480,460,700,990,42.11K, 40,1640,540,760,940,42.93K, 40,1800,840,780,1085,40.97K, 200,1840,920,1020,1030,41.31K, 0,0,0,0,40,44.29K, 0,0,20,20,100,44.42K, 0,0,0,60,140,44.11K, 0,0,0,220,245,44.68K, 0,40,80,320,355,44.06K, 0,120,80,280,435,44.24K, 0,120,80,260,400,44.38K, 0,160,180,300,415,44.53K, 80,240,180,260,465,43.79K, 80,160,420,360,505,43.16K, 120,200,400,560,450,43.01K, 240,400,300,300,600,43.24K, 0,0,0,0,0,46.35K, 0,0,0,60,1805,46K, 0,40,0,780,3690,43.73K, 0,920,0,2100,5895,41.62K, 40,7200,300,5960,8620,35.09K, 40,15.8K,1260,8140,9985,27.24K, 120,18K,1460,8500,9915,25.74K, 120,21.84K,2040,8680,10.67K,23.14K, 280,24K,2400,9320,10.71K,20.97K, 440,30.04K,3420,10.08K,10.78K,15.93K, 520,34.64K,4900,10.92K,11.07K,11.13K, 1280,42.72K,6000,11.34K,11.71K,4485, 2360,47.56K,7800,11.9K,11.38K,0, 0,0,0,100,1325,45.59K, 0,160,0,480,2710,43.88K, 0,600,0,1840,4100,42.94K, 40,5400,240,4660,6505,37.64K, 120,11.44K,840,6020,7560,31.73K, 120,13.92K,1580,6780,7365,29.94K, 0,16.36K,1740,7100,7480,28.49K, 80,18.4K,1960,7040,7910,26.51K, 280,22.32K,2960,7700,8145,23.02K, 800,26.52K,3580,8380,8225,20.18K, 760,32.08K,5680,8760,8510,14.09K, 2080,35.04K,6580,8360,8660,10.6K, 0,0,0,60,1220,45.85K, 0,160,0,480,2540,45.02K, 0,640,40,1640,3950,42.66K, 0,5120,260,4020,5865,38.15K, 0,10.4K,860,5440,6555,33.61K, 200,12.76K,1320,5600,6730,32.19K, 160,14.4K,1380,6040,7110,30.01K, 120,16.12K,2080,6720,7425,29.12K, 560,19.8K,3040,6860,7340,25.78K, 680,23K,3960,7440,7875,21.81K, 720,28.16K,5380,7800,7910,17.28K, 1520,30.08K,6280,7680,7895,14.29K, 0,0,0,40,765,46.05K, 0,200,0,280,1810,44.85K, 0,640,0,1040,2505,44.55K, 0,4080,180,2960,3735,41.33K, 40,7400,660,4300,4515,37.51K, 80,8680,780,4320,4480,36.41K, 120,10.12K,1220,4720,4445,35.3K, 160,11.28K,1100,4560,4635,33.57K, 480,14.4K,2000,4860,4725,32.09K, 440,16.68K,2440,4500,5055,29.82K, 760,20.2K,4320,4960,5095,26.13K, 1120,22.08K,4640,5160,4945,24.32K, 0,0,0,20,225,46.76K, 0,0,0,60,580,45.78K, 0,80,0,320,785,46.12K, 0,560,20,900,1200,45.53K, 0,1680,140,1120,1360,43.56K, 120,2360,340,960,1360,43.2K, 0,2480,520,1300,1435,43.03K, 0,2840,600,1380,1485,43.11K, 120,3240,700,1320,1600,42.2K, 120,3560,940,1380,1700,41.66K, 280,4360,1540,1560,1620,40.92K, 360,4720,1600,1640,1720,39.89K, 0,0,0,0,130,45.94K, 0,0,0,20,290,45.81K, 0,0,0,160,450,45.52K, 0,400,60,420,640,45.48K, 0,840,180,500,855,45.87K, 0,1000,220,540,945,43.98K, 80,1120,140,540,875,43.82K, 0,1320,260,680,820,43.97K, 40,1360,480,720,940,44.77K, 40,1680,500,560,1180,44.19K, 240,2120,880,760,1040,42.67K, 280,2120,1020,960,855,43.16K, 0,0,0,0,30,46.8K, 0,0,20,0,115,45.89K, 0,0,0,20,175,45.75K, 0,80,40,180,300,45.93K, 0,80,100,220,360,44.52K, 0,160,20,300,400,45.67K, 40,80,180,260,435,45.02K, 40,120,100,360,490,45.05K, 80,200,220,320,455,46.13K, 120,240,240,360,450,44.62K, 120,280,380,480,495,45.75K, 160,320,340,460,445,44.31K, 0,0,0,0,0,46.06K, 0,0,0,100,1670,44.8K, 0,120,0,920,3490,43.52K, 0,880,0,2540,5590,41.08K, 0,7400,260,5900,8585,34.17K, 0,16.08K,1140,8280,9800,27.45K, 40,17.8K,1840,9220,10.05K,25.98K, 80,21.76K,1740,9280,10.27K,23.17K, 440,24.08K,2700,9820,10.56K,20.55K, 400,30.44K,3500,9720,11.64K,16.35K, 800,34.4K,4860,10.72K,11.56K,11.4K, 1240,42.52K,6740,11.66K,11.39K,4775, 2360,48.04K,7940,11.6K,12.3K,0, 0,0,0,80,1440,45.12K, 0,120,0,540,2585,45.83K, 0,840,20,1820,4375,42.29K, 0,6080,240,4340,6480,37.33K, 40,12.12K,1040,6540,7445,32.48K, 120,13.2K,1220,6700,7715,30.61K, 80,16.76K,1740,6660,7595,27.87K, 120,18.64K,2180,6800,7945,26.21K, 240,22K,3120,7900,8065,23.47K, 640,26.04K,3720,8440,8475,19.54K, 1200,32.84K,5660,8220,8545,14.24K, 1640,35.56K,6900,8680,8790,11.07K, 0,0,0,80,1060,45.06K, 0,200,20,380,2740,44.21K, 0,880,0,1760,3765,42.57K, 0,5080,100,4260,5940,38.55K, 0,10.52K,700,5840,6685,33.37K, 40,12.44K,1040,5920,6905,31.61K, 200,14.72K,1340,6280,6920,29.57K, 360,17.12K,1600,6600,6860,28.58K, 280,20.24K,2180,7080,7400,25.43K, 1080,23.08K,3540,7380,7505,21.6K, 1080,28.56K,5400,7780,7715,17.25K, 1720,30.52K,6000,7840,7750,14.66K, 0,0,0,20,830,46.39K, 0,40,20,300,1770,44.18K, 0,440,0,1200,2715,43.87K, 40,3560,200,3220,3925,40.4K, 40,7880,780,4060,4170,36.83K, 40,9160,780,4120,4520,35.78K, 160,10.04K,1120,4820,4610,35.17K, 160,11.84K,1300,4520,4875,33.86K, 320,14.12K,1880,5060,4970,32.66K, 600,16.64K,2720,5040,4675,29.26K, 680,19.52K,4000,5180,5090,26.06K, 1200,21.88K,4820,5540,5180,23.42K, 0,0,0,20,245,45.96K, 0,0,0,100,470,45.17K, 0,80,0,360,790,45.31K, 0,880,100,560,1155,44.55K, 0,1880,320,1080,1310,42.78K, 40,2160,240,940,1475,43.59K, 0,2360,380,1180,1570,43.36K, 0,2560,480,1400,1510,43.04K, 80,3160,760,1280,1730,42.24K, 120,3400,1360,1540,1760,42.34K, 160,4440,1360,1400,1600,40.76K, 400,4880,1720,1680,1615,40.26K, 0,0,0,20,155,46.25K, 0,0,0,40,245,46K, 0,40,20,80,545,45.92K, 0,280,60,440,670,44.58K, 0,760,200,540,810,44.34K, 0,1040,180,440,935,43.92K, 0,1120,340,560,955,43.87K, 160,1080,320,660,945,44.39K, 0,1400,420,780,970,44.22K, 0,1720,640,760,1100,43.97K, 160,2160,900,860,945,43.43K, 280,2280,960,860,1070,43.2K, 0,0,0,0,50,46.01K, 0,0,0,0,50,46.14K, 0,0,0,20,230,46.6K, 0,40,0,80,305,46.67K, 0,120,60,140,425,44.72K, 0,120,100,280,450,44.76K, 0,160,100,300,440,46.05K, 40,120,120,420,460,44.94K, 80,120,260,460,450,45.37K, 40,280,320,220,455,45.39K, 80,440,360,320,510,45.58K, 160,280,440,460,480,44.44K, 0,0,0,0,0,45.23K, 0,0,0,80,1775,43.86K, 0,160,0,660,3735,42.62K, 0,1000,0,2260,5685,40.14K, 40,6880,340,5700,8540,33.28K, 0,16.4K,1140,7720,9655,26.2K, 40,18.84K,1420,8460,10.16K,24.79K, 120,21.84K,2080,8600,10.32K,22.42K, 160,24.84K,2360,8840,10.6K,20.77K, 280,28.8K,3380,10.34K,11.05K,15.82K, 600,33.92K,4200,10.96K,11.26K,11.11K, 880,41.72K,6500,12.06K,11.88K,4455, 1800,46.76K,7400,12.28K,11.6K,0, 0,0,0,100,1380,44.72K, 0,40,0,440,2885,42.82K, 0,800,0,2100,4155,41.35K, 0,5960,340,4740,6355,36K, 120,12.36K,680,6080,7465,30.99K, 80,14.08K,1200,6280,7460,28.86K, 120,16.16K,1460,6540,7620,27.53K, 120,18.56K,2160,7300,7715,26.24K, 320,22.44K,2700,7680,7805,22.91K, 680,25.4K,3460,8320,8245,19.02K, 800,32.04K,5380,8420,8510,13.8K, 1480,35.28K,6180,8600,8470,10.62K, 0,0,0,40,1145,44.65K, 0,80,0,520,2580,44.26K, 0,720,20,1520,3885,42.05K, 40,5680,280,3940,5665,36.84K, 0,10.76K,900,5600,6435,32.17K, 200,12.32K,1360,5240,6975,30.95K, 80,14.56K,1520,6460,7210,29.61K, 320,16.2K,1600,6220,7450,27.39K, 320,20.08K,2520,6500,7550,24.21K, 640,24.04K,3380,7420,7240,21.18K, 920,28.52K,4820,7580,8160,17.35K, 1400,29.6K,6280,8220,8450,13.92K, 0,0,0,80,800,44.32K, 0,40,0,340,1570,44.51K, 0,680,0,940,2435,42.63K, 40,3640,260,2880,3725,39.95K, 0,8120,600,4180,4255,36.49K, 80,9640,580,3700,4585,35.59K, 120,10.32K,1120,4580,4425,34.13K, 80,11.52K,1340,4620,4885,33.33K, 240,13.84K,1980,4640,4845,30.8K, 280,16.08K,2380,5280,4965,28.02K, 800,19.16K,3920,5180,5130,25.11K, 1120,20.72K,4820,5380,5170,23.14K, 0,0,0,0,230,44.61K, 0,0,0,120,450,44.82K, 0,80,0,280,750,44.14K, 0,800,80,640,1180,44.88K, 0,2000,320,880,1375,42.62K, 40,2200,460,1020,1445,42.69K, 40,2160,380,1260,1405,41.76K, 160,2560,560,1260,1500,41.21K, 80,3120,840,1480,1505,41.49K, 0,3600,1200,1320,1545,41.39K, 120,4280,1180,1420,1475,39.89K, 360,4760,1580,1400,1740,38.06K, 0,0,0,0,175,44.73K, 0,0,0,40,270,44.23K, 0,0,0,160,405,44.34K, 0,240,40,560,630,43.53K, 0,680,60,520,935,44.09K, 0,880,200,620,850,43.23K, 0,1320,300,420,955,43.3K, 80,1000,380,720,820,42.9K, 120,1440,360,720,990,41.81K, 240,1840,300,640,1025,42.88K, 200,2160,740,680,1100,41.93K, 120,2120,980,880,1240,42.63K, 0,0,0,0,70,44.58K, 0,0,0,0,70,44.84K, 0,0,0,40,180,45.21K, 0,0,0,160,320,44.86K, 0,80,40,260,335,44.07K, 0,120,60,400,350,44.33K, 0,120,100,300,365,43.68K, 0,160,180,340,495,44.58K, 40,240,80,300,490,43.19K, 0,320,280,300,460,43.31K, 80,360,300,380,530,43.39K, 160,400,240,400,500,43.8K, 0,0,0,0,0,42.7K, 0,0,0,100,1660,41.61K, 0,160,0,600,3680,40.46K, 0,1160,20,1860,5550,39.19K, 0,6520,240,5880,8285,32.73K, 40,15.32K,800,8260,9645,25K, 0,17.92K,1220,8320,9955,23.61K, 80,20.56K,1700,8620,10.54K,21.6K, 200,23.28K,2140,9720,10.19K,19.57K, 520,29.08K,2800,10.12K,10.55K,14.75K, 880,33.76K,3980,10.18K,11.35K,10.74K, 1320,39.88K,5820,11.46K,11.73K,3875, 1400,45.92K,7120,11.56K,11.55K,0, 0,0,0,80,1240,43.07K, 0,40,20,500,2780,41.68K, 0,680,0,1700,4370,39.64K, 0,5520,100,4400,6390,35.21K, 0,11.2K,720,6140,7345,29.8K, 120,13.08K,940,6600,7750,27.76K, 40,15.36K,1460,6680,7385,26.63K, 200,16.52K,1820,7380,7640,25.02K, 320,22.04K,2620,7420,7880,21.98K, 560,24.44K,3200,8060,8380,18.15K, 760,31.68K,5040,7860,8550,13.34K, 1440,34.4K,5740,8920,8385,10.1K, 0,0,0,60,1100,42.41K, 0,160,0,460,2500,41.96K, 0,600,0,1520,3825,39.7K, 0,4800,120,4040,5660,35.8K, 0,9640,700,5840,6575,31.25K, 40,11.84K,920,5600,6765,28.89K, 200,14.24K,1300,5960,7030,27.92K, 200,15.6K,1740,6600,7095,27.08K, 280,19.36K,2700,6640,7335,23.47K, 800,22.16K,2920,7640,7720,20.56K, 1040,27.96K,4720,7520,7995,16.63K, 1160,30.68K,5580,7700,7670,13.25K, 0,0,0,80,725,43.27K, 0,120,0,380,1590,41.74K, 0,520,40,1000,2465,41.33K, 0,3520,100,2880,3710,37.73K, 120,7640,360,3960,4285,35.12K, 40,8240,760,4280,4450,33.4K, 120,9160,1000,4660,4610,32.82K, 160,11.04K,1220,4540,4660,31.12K, 400,13.32K,1640,4440,5025,28.89K, 320,16.08K,2680,4960,4930,27.19K, 720,18.84K,3440,5240,4840,24.09K, 1040,20.36K,3680,5380,5075,22.49K, 0,0,0,20,245,42.94K, 0,0,0,120,505,42.56K, 0,40,0,260,720,42.27K, 0,880,40,760,1120,41.64K, 0,1800,180,1040,1320,40.47K, 0,2080,320,1180,1360,40.52K, 0,2080,480,1240,1435,39.99K, 0,2400,480,1100,1540,39.51K, 80,2960,580,1480,1480,38.88K, 200,3320,860,1400,1635,38.52K, 120,3680,1200,1680,1790,37.14K, 240,4520,1480,1400,1730,37.23K, 0,0,0,0,130,42.87K, 0,0,0,60,210,43.37K, 0,0,20,140,420,42.97K, 0,400,40,480,610,42.46K, 0,680,120,560,790,41.78K, 40,1000,220,480,965,42.2K, 0,1080,300,540,755,40.9K, 0,1080,200,600,985,41.34K, 0,1400,380,620,915,41.04K, 120,1760,600,740,1025,40.73K, 200,1800,720,800,1065,39.94K, 200,2160,760,920,1020,39.29K, 0,0,0,0,45,42.6K, 0,0,0,0,80,43.16K, 0,0,0,20,210,43.39K, 0,0,20,180,320,43.05K, 0,120,140,180,390,41.85K, 0,160,40,260,395,42.85K, 0,160,120,240,375,42.21K, 0,280,180,240,430,42.84K, 40,280,180,240,450,42.05K, 40,320,240,380,435,42.15K, 80,360,200,340,465,41.55K, 240,320,260,340,535,41.52K, 0,0,0,0,0,41K, 0,0,0,60,1750,40.05K, 0,40,0,560,3545,39.01K, 0,880,0,2020,5195,36.48K, 0,6320,200,5720,7965,30.91K, 0,14.04K,1080,7740,9810,25.35K, 120,16.92K,860,8320,9375,22.54K, 160,19.6K,1400,8860,10.17K,20.86K, 120,22.48K,1600,8800,10.56K,18.7K, 160,26.68K,3040,10.28K,11.07K,14.56K, 440,31.92K,3720,10.5K,11.11K,10.58K, 800,39.44K,4980,11.58K,11.35K,4165, 1160,44.92K,6740,10.84K,11.58K,0, 0,0,0,140,1210,41.28K, 0,120,20,460,2600,39.39K, 0,440,0,1860,4060,38.12K, 0,4440,260,4480,6260,33.84K, 40,11.04K,800,5460,7440,28.43K, 0,13.32K,940,6400,7220,26.91K, 40,14.16K,1480,7100,7310,24.8K, 120,17.4K,1320,7140,7845,23.94K, 240,20.96K,2140,7220,7735,20.87K, 360,23.4K,3140,7920,8350,17.59K, 720,30.4K,4500,8140,8425,12.99K, 1160,33.76K,4800,8840,8350,9665, 0,0,0,20,1080,40.9K, 0,40,0,380,2455,39.37K, 0,400,0,1560,3595,38.4K, 0,4320,300,3760,5775,34K, 40,8920,580,5380,6740,29.32K, 120,11.28K,1000,5820,6465,28.56K, 80,12.96K,960,6100,6955,26.81K, 200,15.08K,1880,6380,7000,25.86K, 240,17.28K,2160,6980,7735,23.15K, 440,21.24K,3040,7000,7580,19.63K, 1200,25.68K,4200,6980,7900,15.56K, 960,29.36K,5120,7900,8120,12.54K, 0,0,0,40,755,41.16K, 0,80,0,420,1525,40.48K, 0,240,0,960,2550,39.55K, 0,3240,100,2880,3945,36.53K, 40,7000,560,3980,4195,33.96K, 0,8000,580,4160,4475,32.52K, 120,9280,960,4240,4535,32.1K, 160,10.36K,1000,4560,4825,30.46K, 200,13.08K,1740,4700,4590,28.12K, 400,14.92K,2260,5300,5065,27.5K, 640,18.04K,2880,5300,5050,23.64K, 920,20.56K,3440,5260,5050,22K, 0,0,0,20,220,41.35K, 0,0,0,160,465,41.45K, 0,40,0,300,615,40.9K, 0,680,60,700,1160,39.23K, 0,1480,80,1140,1280,38.78K, 0,1760,200,1140,1330,39.13K, 0,2360,340,1100,1455,38.8K, 40,2280,460,1260,1530,38.79K, 80,2760,660,1400,1500,37.41K, 40,3280,820,1380,1515,37.41K, 160,3840,1280,1340,1750,36.1K, 240,3960,1420,1420,1845,35.07K, 0,0,0,0,100,40.69K, 0,0,0,60,220,41.13K, 0,40,0,120,425,41.02K, 0,360,100,280,660,41.23K, 0,800,100,460,790,39.86K, 0,880,160,640,860,39.61K, 0,720,220,780,850,39.97K, 40,1280,260,580,900,39.97K, 80,1400,320,620,990,39.74K, 0,1680,560,680,1000,38.43K, 200,1720,680,800,945,38.22K, 160,2120,780,760,925,38.63K, 0,0,0,0,25,41.3K, 0,0,0,0,100,40.96K, 0,0,0,20,180,41.54K, 0,40,0,180,245,41.99K, 0,80,120,220,385,41.16K, 0,120,140,240,420,41.05K, 0,0,80,260,465,40.51K, 40,160,60,140,525,40.45K, 40,120,140,320,400,40.97K, 120,240,160,400,465,40.42K, 80,320,240,280,540,41.39K, 0,280,340,320,525,39.78K, 0,0,0,0,0,41.3K, 0,0,0,40,1605,39.78K, 0,120,0,440,3395,38.98K, 0,560,0,1940,5445,36.32K, 0,6120,340,5440,7920,30.52K, 40,13.92K,740,6960,9945,24.32K, 80,15.76K,1160,8080,10.17K,22.18K, 120,19.68K,1220,8360,10.42K,20.59K, 80,20.56K,1840,9440,10.42K,18.68K, 240,26.12K,2420,9660,10.51K,14.16K, 480,31.36K,3900,9980,11.19K,10.16K, 1000,37.96K,5360,11.3K,11.27K,4055, 1400,43.12K,6480,11.46K,11.42K,0, 0,0,0,80,1170,41K, 0,40,0,520,2605,38.75K, 0,600,0,1320,4090,37.17K, 0,5360,180,3840,6185,33.22K, 0,10.76K,700,5800,7165,27.65K, 80,12.48K,880,6520,7210,27.26K, 40,14.36K,1460,6240,7415,25.47K, 200,17K,1480,6960,7600,23.95K, 320,20K,2020,7280,7835,20.23K, 280,23.28K,3280,7540,8040,17.54K, 840,29.36K,4320,8560,8350,12.32K, 920,32.48K,5120,8080,8840,8935, 0,0,0,20,1015,40.7K, 0,240,0,280,2410,39.93K, 0,440,40,1660,3490,38.63K, 0,3960,220,3620,5830,33.04K, 80,9520,660,5080,6405,29.51K, 0,11.04K,1100,5580,6415,27.84K, 40,13.12K,1440,5460,6885,26.28K, 120,14.16K,1700,6340,6765,24.4K, 240,17.88K,2240,6420,7440,22.48K, 360,21.48K,3180,7000,7245,19.19K, 680,25.28K,4060,7520,7430,15.37K, 880,28.08K,5020,7580,7920,12.44K, 0,80,0,40,835,39.87K, 0,120,0,380,1425,40.01K, 0,400,40,1040,2630,39.39K, 40,2920,140,2740,3870,35.92K, 80,6360,420,4000,4370,32.83K, 40,7600,780,4040,4475,32.05K, 120,9000,800,4360,4265,30.53K, 120,10.16K,1260,4360,4810,29.65K, 240,12.72K,1360,4860,4485,28.35K, 360,15.2K,1880,4800,4840,25.72K, 600,17.84K,2780,4940,5105,22.73K, 1040,19.32K,3600,5380,5200,20.82K, 0,0,0,0,185,41.35K, 0,0,0,80,485,40.39K, 0,160,0,180,775,40.73K, 0,640,20,640,1205,39.78K, 0,1480,200,980,1280,37.88K, 0,1720,220,980,1470,38.95K, 0,2320,360,1020,1495,38.21K, 0,2320,360,1240,1545,37.5K, 40,2760,420,1260,1525,37.35K, 40,3440,800,1300,1480,36.66K, 240,4080,1120,1240,1635,35.55K, 120,4120,1160,1620,1590,35.04K, 0,0,0,20,110,40.63K, 0,0,0,60,255,40.65K, 0,40,0,80,340,41.73K, 0,200,20,340,730,39.69K, 0,560,80,580,900,39.14K, 0,760,180,580,945,39.66K, 40,800,260,680,835,39.21K, 0,960,400,640,820,39.31K, 40,1400,460,640,840,38.2K, 80,1480,640,620,935,38.53K, 160,1800,640,820,1040,38.07K, 200,1840,820,840,1095,37.92K, 0,0,0,0,15,40.55K, 0,0,0,0,120,41.01K, 0,0,20,20,160,40.33K, 0,40,0,80,295,39.9K, 0,0,60,280,410,39.7K, 0,160,60,160,395,40K, 40,160,40,240,380,39.2K, 0,120,100,280,365,39.85K, 40,160,140,320,360,39.95K, 0,240,200,340,480,39K, 40,400,300,340,395,40.39K, 40,200,340,480,455,39.07K, 0,0,0,0,0,40.55K, 0,0,0,100,1580,40.82K, 0,80,0,620,3275,38.91K, 0,560,0,2120,5165,37.19K, 0,5800,80,5660,8065,31.11K, 0,13.88K,700,7180,9320,24.54K, 0,15.8K,980,8220,9800,22.56K, 160,19.08K,1440,8580,10.23K,20.85K, 160,20.48K,1760,9240,10.45K,18.69K, 320,26.36K,2920,10.04K,9950,14.3K, 360,30.52K,4140,9900,11.06K,10.5K, 840,37.72K,5100,11.32K,11.24K,4290, 1640,42.28K,6280,11.96K,11.94K,0, 0,0,0,40,1090,40.81K, 0,160,0,480,2545,39.57K, 0,440,20,1380,4090,37.28K, 0,5120,220,3860,6040,33.77K, 0,10.24K,580,5860,7310,29.29K, 80,12.48K,820,6080,7210,27.38K, 120,13.4K,1380,7160,7435,25.77K, 160,15.92K,1540,6540,7985,23.49K, 240,20.24K,2160,6980,7800,20.25K, 200,23.28K,3100,7600,8085,17.48K, 1040,29.4K,4200,8620,8340,12.77K, 1040,31.4K,5620,8800,8410,9905, 0,0,0,80,895,39.64K, 0,40,0,400,2375,39.82K, 0,640,40,1560,3555,38.7K, 0,4080,200,4140,5560,35.05K, 0,9520,820,5320,6630,29.25K, 120,10.96K,860,5480,6970,27.95K, 160,12.08K,1360,5660,6950,27.23K, 120,13.76K,1540,6200,6790,25.27K, 400,17.4K,2220,6580,7390,23.23K, 400,20.16K,2880,7060,7700,20.27K, 680,25.52K,4160,7640,7690,14.88K, 1120,27.52K,5180,8140,7865,12.45K, 0,0,0,60,815,41.43K, 0,160,0,280,1640,39.95K, 0,480,20,860,2615,39.64K, 0,3240,60,2800,3510,36.8K, 0,6880,500,3760,4345,33.77K, 40,7960,680,3780,4650,32.6K, 120,8920,820,4440,4260,30.65K, 200,10.56K,1080,4200,4625,30.08K, 200,12.76K,1540,4600,4690,28.22K, 600,15.16K,2000,4920,4980,26.24K, 560,17.96K,3220,4920,5085,23.44K, 800,19K,3840,5320,5200,21.17K, 0,0,0,0,195,41.23K, 0,40,0,40,470,41.8K, 0,0,0,260,840,40.53K, 0,640,80,700,1180,39.97K, 0,1400,380,800,1315,38.8K, 40,1880,280,1080,1220,38.74K, 80,2160,220,1120,1365,38.49K, 0,2520,460,1060,1435,38.69K, 40,2680,460,1180,1565,37.21K, 80,3160,940,1500,1605,36.62K, 160,3960,940,1660,1540,36.22K, 280,4160,1580,1500,1615,35.54K, 0,0,0,0,140,41.24K, 0,0,0,40,265,41.8K, 0,0,0,100,330,40.48K, 0,360,20,420,595,39.89K, 0,560,160,560,865,40.14K, 0,720,220,600,940,40.73K, 0,840,260,640,805,39.95K, 0,880,280,660,850,39.66K, 0,1240,500,720,980,38.48K, 80,1520,600,540,925,39.69K, 120,1720,560,1020,1055,38.72K, 200,2080,780,680,1015,38.14K, 0,0,0,0,45,40.7K, 0,0,0,0,120,41.45K, 0,0,20,0,145,40.6K, 0,40,0,100,335,41.43K, 0,120,100,240,385,40.96K, 0,80,100,240,410,40.54K, 40,120,20,260,445,40.62K, 0,240,120,200,365,40.27K, 40,120,160,380,400,40.92K, 40,240,140,340,455,41.51K, 80,400,260,360,475,40.12K, 80,360,340,320,500,40.37K, 0,0,0,0,0,43.25K, 0,0,0,80,1695,42.19K, 0,160,0,560,3300,40.67K, 0,760,60,1920,5425,39.47K, 0,6120,260,5640,7830,32.25K, 40,14.4K,960,7480,9330,26.15K, 0,16.76K,1020,8340,9930,24.24K, 120,20.08K,1660,8860,9770,21.76K, 240,21.52K,1960,8700,10.25K,19.2K, 240,26.84K,3560,9840,11.13K,15.18K, 640,31.04K,4320,9680,11.24K,10.92K, 1200,38.88K,6320,11.28K,11.52K,4195, 1480,44.2K,7420,11.44K,11.37K,0, 0,0,0,40,1040,43.18K, 0,0,0,500,2650,40.83K, 0,680,0,1460,4085,40.14K, 0,5080,100,4400,5910,35.03K, 40,10.52K,720,5680,7165,30.43K, 40,13.2K,1040,6040,7435,27.69K, 120,14.32K,1240,6900,7915,26.74K, 160,16.36K,1800,6800,7495,24.78K, 80,20.92K,2700,7080,8045,21.9K, 440,23.4K,3140,8200,8250,18.21K, 1160,30.04K,4840,8340,8120,13.35K, 1160,32.4K,5420,9180,8430,10.1K, 0,0,0,20,1020,42.78K, 0,80,0,360,2450,41.1K, 0,520,20,1380,3865,41.11K, 0,4480,280,3880,5440,35.43K, 0,9800,780,5140,6400,31.5K, 0,11.36K,1100,5280,6495,29.49K, 160,12.8K,1220,5700,6710,28.21K, 200,14.4K,1500,6080,7115,26.6K, 320,18.04K,2460,6420,7285,23.43K, 440,20.44K,3420,7200,7625,20.42K, 680,26.32K,4360,7800,7450,16.19K, 1160,29K,5340,8060,7715,13.39K, 0,0,0,60,745,43.78K, 0,80,0,360,1650,42.23K, 0,320,20,880,2540,40.43K, 0,3080,160,2660,3575,37.71K, 0,6360,560,4060,4425,35.06K, 80,8360,660,3880,4595,33.68K, 40,9280,780,4260,4670,33.11K, 80,10.84K,1420,4280,4495,31.11K, 200,12.72K,1800,4660,5170,29.37K, 400,15.4K,2420,5100,4935,27.66K, 760,18.64K,3140,4680,5175,24.21K, 800,20.76K,3980,5100,5065,21.79K, 0,0,0,0,220,42.69K, 0,0,0,40,420,42.63K, 0,40,0,320,660,42.6K, 0,640,80,660,1165,41.53K, 0,1280,180,960,1380,40.54K, 40,2080,220,920,1380,40.24K, 40,2240,440,880,1595,39.86K, 40,2480,440,1240,1540,39.73K, 40,2600,720,1540,1390,39.98K, 120,3400,880,1320,1500,38.35K, 200,4040,1220,1520,1640,38.23K, 400,3760,1640,1660,1560,37.12K, 0,0,0,0,135,42.37K, 0,0,0,20,370,43.74K, 0,0,0,80,440,42.6K, 0,320,80,460,630,42.35K, 0,640,140,640,820,41.07K, 80,920,120,560,970,41.91K, 0,920,200,520,880,40.77K, 0,960,300,640,940,41.82K, 40,1480,360,700,955,41.43K, 120,1600,680,660,1050,40.12K, 120,1800,680,820,1035,40.72K, 280,1920,900,880,935,40.38K, 0,0,0,0,45,43.14K, 0,0,0,0,75,42.99K, 0,0,0,40,160,43.22K, 0,0,0,60,370,42.32K, 0,40,80,220,445,42.71K, 0,80,60,280,395,42.56K, 40,120,80,200,405,41.51K, 0,280,140,180,450,41.55K, 40,80,220,340,435,41.99K, 120,120,240,500,460,42.36K, 80,240,320,380,495,41.83K, 40,320,360,360,575,41.39K, 0,0,0,0,0,45.93K, 0,0,0,60,1605,44.3K, 0,80,0,720,3505,42.83K, 0,920,20,2100,5515,41.68K, 40,6560,280,5880,8115,33.22K, 40,15.28K,1040,8080,9245,27.56K, 120,17.84K,1460,8360,10.04K,24.93K, 160,21.04K,1880,8300,10.31K,22.72K, 360,22.72K,2300,8900,10.6K,20.7K, 280,28.28K,3560,10.3K,10.75K,15.67K, 520,32.72K,4140,10.54K,11.31K,11.41K, 1200,40.48K,6920,11.24K,11.58K,4470, 1480,45.28K,7720,11.98K,11.6K,0, 0,0,0,20,1065,44.81K, 0,80,0,540,2490,43.3K, 0,560,20,1860,4150,41.01K, 0,5160,280,4220,6380,36.8K, 40,11.12K,700,6340,7150,30.89K, 120,13.64K,1300,6220,7375,29.69K, 160,15.12K,1240,6820,7535,28.04K, 200,18.4K,1740,6740,8095,26.28K, 360,21.6K,2760,7680,8135,22.54K, 440,25.76K,3400,8040,7820,19.38K, 1440,30.92K,5060,8400,8340,14.1K, 1480,35.52K,6400,8120,8445,10.39K, 0,0,0,80,1070,44.81K, 0,80,0,540,2565,43.63K, 0,720,20,1720,3795,41.9K, 0,4680,220,4120,5385,37.89K, 80,10.08K,740,5140,6715,33.51K, 40,11.52K,1340,6060,6710,30.69K, 240,13.72K,1540,5860,7020,29.35K, 120,15.52K,2040,6660,7015,28.33K, 520,18.64K,2660,7100,7310,24.75K, 640,22.08K,3880,7020,7635,21.84K, 1040,27.24K,5320,7300,7950,17.69K, 1320,29.6K,6800,7440,8045,13.86K, 0,0,0,60,835,44.32K, 0,80,0,300,1880,44.17K, 0,360,0,1040,2630,42.63K, 0,3120,240,2880,4055,39.36K, 40,7360,540,3960,4155,35.44K, 0,8800,860,3980,4460,35.77K, 120,9800,860,4280,4725,34.55K, 120,11.28K,1340,4720,4555,32.34K, 240,13.44K,1620,4720,5010,31.01K, 320,16.04K,2660,4900,5095,28.99K, 1080,19.36K,3560,4820,5150,25.61K, 1000,20.96K,4800,5180,5095,23.73K, 0,0,0,0,230,44.69K, 0,0,0,40,460,45.02K, 0,120,0,380,680,45.25K, 0,560,80,760,1200,43.58K, 0,1680,300,900,1290,42.92K, 40,2080,240,1020,1320,42.13K, 40,2480,460,1220,1350,42.02K, 120,2520,440,1340,1440,42.22K, 40,2960,700,1380,1495,41.17K, 200,3440,720,1440,1645,40.28K, 160,4320,1220,1660,1540,39.77K, 400,4400,1620,1480,1670,39.46K, 0,0,0,0,130,45.52K, 0,0,0,40,310,45.43K, 0,0,20,120,460,44.45K, 0,400,20,360,680,44.79K, 0,920,120,620,760,42.52K, 40,920,240,680,895,43.2K, 40,960,260,700,880,43.01K, 0,1040,320,740,870,43.64K, 120,1520,300,660,1000,42.88K, 40,1800,640,680,935,42.79K, 200,2000,720,760,1070,41.31K, 120,2120,860,740,1030,42.16K, 0,0,0,0,40,45.39K, 0,0,0,0,60,45.63K, 0,0,0,20,165,44.28K, 0,0,20,120,360,44.35K, 40,40,40,220,410,45.28K, 0,80,140,180,425,44.59K, 0,160,200,240,430,44.4K, 0,160,200,280,400,43.45K, 40,200,180,340,515,43.98K, 40,320,260,300,445,44.58K, 200,160,300,420,525,44.02K, 200,400,440,400,515,43.38K, 0,0,0,0,0,46.92K, 0,0,0,140,1730,46.76K, 0,160,0,680,3665,44.14K, 0,1000,0,2060,5740,42.37K, 0,6840,380,5900,8275,35.01K, 200,15.72K,1180,8480,9865,28.47K, 120,18.64K,1380,8260,10.23K,25.75K, 40,22.32K,2220,9080,10.25K,22.95K, 400,23.68K,2360,9620,10.74K,21.33K, 320,29.64K,3380,9960,11.4K,16.61K, 600,36.44K,5140,10.96K,11.14K,11.82K, 1560,42.36K,6960,11.72K,11.65K,4690, 1920,47.56K,7900,11.82K,11.77K,0, 0,0,0,100,1130,46.04K, 0,80,0,600,2935,44.96K, 0,560,40,1700,4545,43.06K, 0,5640,420,4740,6335,37.81K, 40,11.84K,1020,6080,7095,32.46K, 80,13.48K,1320,6360,7465,30.21K, 200,16.92K,1640,6780,7165,28.91K, 160,18.44K,2140,7640,7695,26.82K, 280,23.48K,2940,7420,8090,23.95K, 720,26.68K,3900,7980,8395,20.36K, 1280,32.56K,5660,8420,8605,14.85K, 1440,36.44K,6740,8420,8850,11.01K, 0,0,0,80,1300,46.97K, 0,120,0,340,2545,45.55K, 0,640,0,1620,4015,43.58K, 0,4960,280,3720,5895,38.75K, 120,10.4K,1120,5420,6720,33.89K, 40,11.76K,1440,6020,7065,32.52K, 80,15.16K,1820,6020,6860,31.01K, 240,16.08K,1920,6440,7265,28.91K, 320,20.04K,3160,6500,7695,25.46K, 480,23.08K,3940,7760,7650,22.77K, 920,27.84K,5920,7480,7835,17.97K, 1560,30.32K,6580,7960,8165,14.27K, 0,0,0,60,825,46.89K, 0,80,0,460,1910,45.12K, 0,600,0,1000,2715,44.27K, 0,3720,240,2780,3850,40.89K, 40,7440,600,4240,4485,37.85K, 40,8520,960,4500,4580,36.93K, 40,10.16K,1220,4760,4775,35.83K, 320,12K,1540,4460,4785,35.08K, 280,14.6K,2220,4480,4580,32.02K, 480,17.4K,2920,4760,4740,30.1K, 800,19.28K,3900,5280,5165,25.76K, 1600,22.36K,4740,5180,5050,24.43K, 0,0,0,0,260,46.91K, 0,0,0,80,450,46.93K, 0,160,0,280,770,45.61K, 0,720,40,740,1120,45.37K, 0,1760,180,1180,1290,43.86K, 0,1880,400,1200,1405,44.18K, 40,2480,500,1200,1545,44.01K, 40,2640,680,1340,1395,43.69K, 120,2960,840,1540,1600,42.26K, 160,3600,980,1440,1595,42.07K, 240,4360,1440,1600,1640,40.2K, 440,4880,1940,1240,1915,40.43K, 0,0,0,0,105,46.4K, 0,0,0,20,275,46.08K, 0,40,0,140,420,46.14K, 0,480,40,440,670,46.62K, 0,840,140,560,845,45.11K, 40,1080,180,560,930,44.54K, 40,1200,260,540,895,44.95K, 0,1240,320,640,955,45.21K, 200,1360,400,720,975,44.82K, 80,1600,460,900,905,43.54K, 200,2080,800,760,1035,43.54K, 200,2000,1240,1120,1000,43.16K, 0,0,0,0,55,47.51K, 0,0,0,0,70,48.03K, 0,0,0,60,145,46.46K, 0,40,40,60,370,46.6K, 0,120,60,140,370,46.65K, 0,120,120,220,385,46.24K, 0,240,120,220,420,46.18K, 40,40,180,360,465,46.16K, 0,280,240,380,480,45.75K, 80,200,320,360,500,46.51K, 240,280,380,360,535,45.22K, 160,280,380,440,480,45.36K, 0,0,0,0,0,49.16K, 0,0,0,180,1745,46.77K, 0,200,0,680,3850,46.13K, 0,920,40,2500,5660,43.14K, 0,7520,480,5940,8305,35.66K, 120,16.36K,1080,8780,9980,29.65K, 80,19.2K,1820,8640,10.05K,25.6K, 200,23.28K,2020,8880,10.57K,24.09K, 120,25.84K,2320,9360,10.42K,21.91K, 520,30.68K,3520,10.58K,11.37K,16.64K, 760,35.68K,4920,11.82K,11.5K,11.86K, 1480,44K,7060,11.38K,11.96K,5020, 2120,49.16K,8660,12.22K,11.95K,0, 0,0,0,80,1315,46.99K, 0,80,0,540,2920,45.75K, 0,640,20,1780,4450,43.79K, 0,5880,260,4680,6385,38.66K, 160,12.36K,940,6440,7600,33.3K, 80,14.32K,1600,6380,7770,30.96K, 120,16.96K,2060,7080,7635,30.18K, 280,19.92K,2420,6800,7465,28.54K, 480,23.28K,3520,8020,8340,23.88K, 520,27.36K,4360,8220,8440,20.57K, 1080,33.92K,6000,8460,8430,14.55K, 1720,37.6K,7820,8740,8670,10.98K, 0,0,0,80,1150,47.57K, 0,80,0,680,2610,46.23K, 0,680,20,1860,3745,43.97K, 0,5680,200,4220,5975,40.08K, 80,10.92K,1060,5500,7075,34.69K, 160,13.36K,1180,6300,6980,33.17K, 240,15.12K,1680,6500,7160,30.67K, 200,17.4K,1980,6480,7135,29.39K, 400,20.72K,3020,6720,7420,26.82K, 720,24.52K,4120,7380,7685,22.68K, 1280,28.8K,6320,7720,7760,18.21K, 1600,32K,7160,7880,7950,14.95K, 0,0,0,20,875,46.61K, 0,80,0,380,1830,47.12K, 0,760,0,1060,2795,46.14K, 0,3480,320,3180,4130,42.81K, 40,7720,740,4040,4500,39.1K, 80,8880,1160,4400,4595,38.01K, 120,11.2K,1500,3980,4535,35.79K, 200,12.08K,1760,4680,4710,35.78K, 360,14.6K,2300,4860,5010,33.49K, 760,16.8K,3160,5060,5065,30.27K, 1000,20.2K,4420,5420,5080,26.83K, 1280,22.36K,5120,5580,5225,24.94K, 0,0,0,0,235,48.09K, 0,0,0,60,490,47.2K, 0,80,0,380,790,47.88K, 0,640,40,960,1185,46.5K, 0,1600,220,1420,1330,46.05K, 0,2200,300,1140,1430,45.21K, 0,2160,440,1320,1585,44.58K, 80,3000,620,1240,1530,44.6K, 80,3200,680,1300,1675,44.7K, 120,3760,860,1280,1635,43.64K, 240,4520,1480,1420,1535,41.57K, 600,4680,1660,1660,1735,41.21K, 0,0,0,0,130,48.67K, 0,0,0,40,230,49.12K, 0,160,20,100,380,46.89K, 0,520,0,520,730,46.88K, 0,720,180,660,840,47.16K, 0,1200,300,560,785,46.21K, 0,1160,360,560,960,45.27K, 40,1440,420,660,915,45.37K, 0,1600,400,720,1000,45.01K, 80,1800,420,780,995,45.29K, 200,1880,700,980,940,45.25K, 240,2240,1200,860,1120,44.48K, 0,0,0,0,35,47.72K, 0,0,0,0,95,47.95K, 0,0,0,60,155,48.3K, 0,0,60,180,290,47.97K, 0,40,100,280,360,47.63K, 0,120,100,220,435,48.19K, 0,240,120,140,475,47.85K, 40,200,180,260,500,47.51K, 120,280,140,280,425,47.26K, 120,240,280,460,455,47.19K, 80,320,380,420,440,46.97K, 320,240,340,420,495,46.56K, 0,0,0,0,0,47.94K, 0,0,0,120,1725,47.45K, 0,120,0,840,3675,46.26K, 0,1160,60,1940,5795,43.47K, 0,8000,300,5700,8715,36.24K, 120,17.32K,1380,8220,9740,28.24K, 120,19.4K,1460,8760,10.43K,26.53K, 120,22.72K,2200,9360,10.55K,24.65K, 240,25.2K,2640,9980,10.98K,21.43K, 760,31.48K,3580,10.58K,10.7K,16.65K, 1000,36.4K,5180,11.38K,11.26K,11.75K, 1520,44.72K,7400,11.28K,11.43K,4665, 1960,48.84K,9340,12.12K,11.68K,0, 0,0,0,140,1260,47.11K, 0,80,0,540,2840,46.45K, 0,560,40,1660,4605,43.82K, 40,6160,360,4620,6700,38.35K, 40,13K,1020,6420,7270,33.61K, 200,14.8K,1200,6680,7420,31.73K, 120,16.96K,1820,7340,7475,30.22K, 120,19.6K,2180,7180,7830,27.46K, 480,23.16K,3000,7780,8545,23.98K, 680,27.6K,4100,8480,8390,19.74K, 1400,35.08K,5700,8120,8700,14.51K, 1920,36.96K,7420,9000,8620,11.08K, 0,0,0,80,1260,47.52K, 0,80,0,420,2795,47.15K, 0,1000,40,1460,3975,44.23K, 0,5560,260,4040,5845,38.99K, 40,12.12K,1020,5500,6485,34.56K, 40,13.36K,1140,6380,7050,32.97K, 240,15.52K,1500,6120,7315,31.99K, 200,17.12K,2000,6260,7165,29.52K, 480,20.92K,2780,6900,7390,25.51K, 760,24.92K,3920,7260,8020,22.49K, 1320,29.48K,5820,7360,8050,18.07K, 1640,32.24K,6760,7700,7875,15.16K, 0,0,0,60,790,47.96K, 0,80,0,580,1595,46.45K, 0,640,0,940,2900,45.18K, 0,3640,160,3140,3960,42.29K, 120,7920,800,4040,4405,40.21K, 80,9320,960,4360,4490,37.64K, 80,10.84K,1400,4660,4390,36.37K, 240,12K,1780,4780,4865,35.41K, 400,15.12K,2360,4760,4970,32.97K, 720,17.24K,3080,5400,5085,31.3K, 1280,20.88K,4180,5180,5135,27.37K, 1640,22.36K,5080,5840,5300,25.24K, 0,0,0,0,225,47.78K, 0,0,0,120,430,47.95K, 0,160,0,380,635,47.65K, 0,920,80,880,1190,46.87K, 40,1800,320,1040,1440,45.34K, 40,2160,280,1220,1505,45.13K, 120,2600,240,1140,1655,44.86K, 0,2960,700,1260,1515,43.85K, 280,3280,840,1360,1615,43.94K, 80,3920,1080,1260,1635,43.45K, 200,4480,1460,1540,1625,42K, 760,4440,1760,1620,1780,41.87K, 0,0,0,0,125,47.64K, 0,0,0,20,250,47.75K, 0,40,0,160,445,48K, 0,400,80,480,790,47.86K, 0,760,280,520,835,47.1K, 0,960,220,700,895,47.32K, 0,1240,220,560,850,46.22K, 0,1480,260,640,895,46.85K, 40,1880,500,640,965,45.9K, 40,1720,680,780,970,44.99K, 120,2000,1020,920,1115,44.92K, 200,2280,1040,900,965,44.18K, 0,0,0,0,40,48.29K, 0,0,0,0,70,47.72K, 0,0,0,20,185,48.11K, 0,40,0,100,360,47.84K, 0,120,120,240,410,47.31K, 0,120,60,280,465,47.53K, 40,80,120,360,415,47.36K, 0,200,120,320,455,46.98K, 80,160,260,260,555,46.66K, 80,360,300,280,515,47.11K, 160,320,320,400,495,46.16K, 200,240,400,420,555,46.27K, 0,0,0,0,0,46.62K, 0,0,0,120,1715,46.17K, 0,120,0,680,3710,45.23K, 0,1120,0,2400,5820,43.04K, 40,7680,280,5700,8815,36.25K, 80,17.08K,1160,8260,10K,28.63K, 40,20.48K,1800,8840,10.09K,25.77K, 80,22.68K,2360,9320,10.44K,23.61K, 240,25.6K,3080,10.02K,10.45K,21.02K, 280,31.76K,3760,10.3K,11.23K,16.55K, 880,37.24K,4840,10.78K,11.33K,12.05K, 1160,44.6K,7020,11.72K,11.34K,4660, 2040,47.6K,8260,11.82K,12.45K,0, 0,0,0,140,1375,46.42K, 0,160,20,560,2615,45.6K, 0,880,0,1980,4355,43.43K, 0,6680,140,4380,6290,38.64K, 80,13.16K,940,6140,7325,33.06K, 160,14.44K,1240,6820,7550,30.57K, 200,17K,1640,7200,7555,27.92K, 320,20.2K,1980,7180,7805,27.46K, 400,23.6K,2820,7760,8150,23.36K, 400,27.52K,4360,8220,8335,19.86K, 1200,34.76K,5360,8260,8660,14.37K, 1800,36.52K,6840,8400,9015,11.14K, 0,0,0,100,1245,46.37K, 0,200,0,540,2570,46.64K, 0,680,40,1660,3920,44.4K, 40,5280,360,3920,5970,38.82K, 80,11.72K,1140,5520,6630,34.16K, 40,13.28K,1320,6180,7005,33.33K, 320,15.04K,1420,6400,6915,30.81K, 120,16.88K,2080,6800,7150,30.13K, 440,20.2K,2720,7200,7650,25.84K, 680,24.4K,4140,7380,7700,22.73K, 1200,28.44K,5800,8120,7845,17.81K, 1640,31.52K,6820,8440,8265,14.61K, 0,0,0,60,755,46.97K, 0,80,0,400,1745,46.26K, 0,720,20,800,2735,44.7K, 0,3400,220,3040,3880,41.91K, 40,7760,600,4100,4480,38.18K, 80,8920,1100,4480,4450,36.88K, 240,11.2K,1160,4540,4330,35.81K, 240,11.96K,1240,4740,5035,34K, 400,14.08K,2240,5260,4655,33.19K, 640,17.48K,2720,5220,4940,29.67K, 920,21.04K,4220,5300,4895,26.43K, 1160,23.28K,5080,5500,5150,24.67K, 0,0,0,20,215,46.94K, 0,40,0,100,455,48.11K, 0,120,0,240,840,46.78K, 0,1000,100,840,1150,45.91K, 0,1960,260,1060,1405,45.14K, 0,2120,320,1240,1350,44.94K, 0,2400,600,1420,1555,44.3K, 0,3120,580,1140,1440,43.62K, 120,3320,840,1500,1395,42.57K, 40,3680,1220,1340,1815,43.11K, 240,4320,1720,1740,1635,41.67K, 400,4760,1660,1720,1580,40.44K, 0,0,0,0,95,46.54K, 0,0,0,20,260,47.55K, 0,40,0,80,465,47.13K, 0,160,60,580,655,46.57K, 0,760,200,540,860,45.36K, 40,1000,180,600,910,46K, 0,1120,260,640,940,46.03K, 40,1320,260,760,915,45.94K, 40,1480,440,760,1015,45.08K, 160,1760,600,820,900,44.85K, 200,2200,920,840,1080,44.09K, 320,2080,1100,800,1170,43.63K, 0,0,0,0,115,48.41K, 0,0,0,0,75,47.54K, 0,0,0,40,145,46.79K, 0,0,20,120,355,47.19K, 40,120,80,220,370,46.97K, 0,200,200,220,385,46.92K, 40,200,180,260,450,47.06K, 0,240,160,240,375,46.59K, 0,200,280,360,400,45.55K, 40,200,380,400,490,46.91K, 160,240,400,420,570,46.08K, 120,440,400,440,425,46.8K, 0,0,0,0,0,46.71K, 0,0,0,100,1870,45.74K, 0,80,0,680,3740,44.05K, 0,1000,40,2640,5690,41.82K, 0,7480,240,5940,8555,34.96K, 40,16.96K,940,8420,9530,27.64K, 40,20.2K,1520,8560,10.06K,25.91K, 200,22.44K,1880,8920,10.58K,22.93K, 320,24.24K,2680,9660,10.92K,20.96K, 320,31.36K,3680,10.14K,10.78K,16.28K, 1000,35.96K,4360,11.66K,11.18K,11.88K, 1160,44.32K,6860,11.18K,11.58K,4565, 1640,47.32K,8360,12.16K,12.03K,0, 0,0,0,40,1330,46.23K, 0,120,20,560,2730,44.59K, 0,1040,0,1740,4505,43.03K, 0,6400,260,4600,6480,37.69K, 120,12.6K,800,6640,7220,32.52K, 40,14.48K,1400,6360,7900,29.78K, 120,17.04K,1560,7480,7475,28.54K, 240,19.4K,2000,7960,7715,26.68K, 400,23.08K,3180,8080,7995,23.43K, 480,28.12K,4100,8200,7855,19.98K, 1120,33K,5600,8840,8605,14.35K, 1600,37.24K,6360,8040,8740,10.7K, 0,0,0,40,1285,46.05K, 0,160,0,640,2610,44.51K, 0,680,0,1640,3895,43.43K, 40,5440,340,4000,5835,38.31K, 0,11.28K,840,5180,6570,33.01K, 120,12.84K,1180,5620,6945,31.65K, 200,14.68K,1520,6580,7205,30.33K, 120,16.76K,2220,6400,7145,29.06K, 480,20.04K,3080,7220,7635,25.73K, 720,23.72K,3600,7220,7630,22.52K, 1280,28.36K,5460,7840,8065,17.16K, 1800,31.84K,6340,7640,7650,14.44K, 0,0,0,100,775,46.2K, 0,120,0,280,1650,45.41K, 0,720,20,1080,2810,44.53K, 0,3560,280,3000,4160,40.85K, 80,7520,800,4100,4370,37.7K, 160,8720,840,4160,4660,36.87K, 120,11.04K,1120,4300,4695,35.24K, 160,11.64K,1600,4560,4850,34.43K, 440,13.88K,2160,4900,4905,31.8K, 560,16.64K,2440,5240,5235,29.83K, 1040,20.88K,4060,5040,4805,26.18K, 1120,22.64K,4820,5420,5325,24.39K, 0,0,0,0,190,46.16K, 0,0,0,60,460,45.94K, 0,120,0,380,800,46.11K, 0,800,100,620,1230,44.97K, 0,1840,320,1100,1405,44.12K, 0,2120,300,1160,1395,43.65K, 40,2320,440,1360,1475,43.12K, 40,2760,560,1160,1650,42.48K, 80,3040,700,1540,1610,41.51K, 40,3640,1180,1400,1685,41.9K, 320,4480,1140,1580,1605,40.42K, 600,5000,1760,1380,1725,40.21K, 0,0,0,20,140,46.75K, 0,0,0,80,270,46.44K, 0,0,0,160,455,45.98K, 0,440,60,360,745,45.93K, 40,920,160,560,770,44.73K, 0,960,180,760,800,44.32K, 0,1120,220,720,945,44.52K, 80,1280,400,600,980,44.45K, 0,1600,360,680,930,44.66K, 40,1720,680,680,1050,43.54K, 160,2160,820,760,1040,43.07K, 440,2240,920,760,1020,41.99K, 0,0,0,0,80,46.38K, 0,0,0,20,85,45.64K, 0,0,0,0,125,45.86K, 0,0,60,140,270,46.83K, 0,160,100,180,400,45.9K, 0,80,100,260,400,45.75K, 0,120,100,240,425,45.61K, 80,120,120,340,415,46.58K, 40,240,280,340,450,45.39K, 0,240,240,280,515,45.42K, 200,320,320,420,430,45.12K, 120,360,400,480,520,45.05K, 0,0,0,0,0,47.29K, 0,40,0,80,1825,45.13K, 0,120,0,580,3660,43.6K, 0,880,0,2460,5895,41.37K, 0,7600,280,5740,8490,34.01K, 0,15.64K,940,7940,9950,27.37K, 160,18.6K,1560,8880,10.32K,24.78K, 160,22.16K,1840,8800,10.41K,23.16K, 240,24.88K,2540,9300,10.53K,20.36K, 600,30.4K,3420,10.26K,11.02K,16.17K, 400,36.16K,4780,10.7K,11.39K,11.53K, 1120,43.56K,6580,11.58K,11.23K,4680, 2040,47.56K,7880,11.72K,11.94K,0, 0,0,0,120,1210,45.15K, 0,160,0,560,2715,43.99K, 0,960,60,2040,4210,43.6K, 40,6160,260,4420,6140,37.23K, 80,11.76K,1100,6300,7310,32.09K, 160,14.44K,1240,6360,7610,30.1K, 40,16.6K,1400,6960,7880,27.9K, 240,19.24K,2240,7500,7540,26.29K, 400,24.6K,3020,7680,7705,23.32K, 880,26.84K,3900,7880,8560,19.68K, 1200,32.28K,5540,8320,8515,14.32K, 1760,35.48K,6060,8760,8595,10.37K, 0,0,0,40,1250,45.59K, 0,120,0,560,2580,44.88K, 0,680,20,1640,3610,42.71K, 0,5120,220,3960,6095,38.14K, 0,10.52K,1000,5960,6695,33.54K, 80,12.88K,1460,5940,6840,31.44K, 240,14.8K,1300,5840,7140,29.92K, 160,16.52K,1760,6760,7215,28.51K, 360,20.2K,2960,6780,7510,25.14K, 560,23.08K,3620,7660,7285,21.57K, 1240,27.92K,5460,7940,7630,17.1K, 1440,30.84K,6360,8620,8140,14.08K, 0,0,0,60,800,45.96K, 0,120,20,320,1740,45K, 0,600,40,1080,2875,43.59K, 0,3480,240,3440,4005,39.78K, 80,7200,800,3980,4620,36.87K, 80,8520,720,4340,4695,36.05K, 120,10.16K,1160,4800,4725,34.91K, 160,11.84K,1260,4580,4755,33.65K, 320,14.28K,2060,4820,5010,31.17K, 520,17.52K,2700,4900,4880,28.92K, 760,19.8K,3580,5600,5025,26.47K, 1120,21.8K,4740,5760,5235,24.18K, 0,0,0,0,185,44.99K, 0,0,0,100,390,45.97K, 0,200,20,240,850,45.36K, 40,680,40,700,1200,43.82K, 40,1920,220,900,1295,43.57K, 0,2240,280,960,1550,42.78K, 40,2200,360,1420,1490,42.38K, 40,2760,620,1140,1505,42.96K, 80,2960,720,1580,1535,41.7K, 80,3600,980,1300,1530,42.06K, 200,4520,1360,1480,1635,40K, 200,4840,1680,1600,1690,39.84K, 0,0,0,0,160,45.33K, 0,0,0,0,245,46.95K, 0,80,0,180,480,45.1K, 0,360,60,480,660,45.35K, 0,840,140,580,855,44.04K, 0,880,320,680,865,44.83K, 40,1200,320,660,940,43.76K, 0,1160,260,740,1000,43.61K, 40,1600,480,860,995,43.96K, 40,1560,620,880,975,42.69K, 160,1880,840,900,1050,43.71K, 200,2000,900,1020,1055,42.67K, 0,0,0,0,45,45.66K, 0,0,0,20,100,45.81K, 0,0,0,20,175,46.7K, 0,40,40,120,280,46.16K, 0,80,80,260,350,45.62K, 40,80,120,300,390,44.98K, 40,120,80,200,395,45.75K, 0,240,80,300,390,44.76K, 0,280,260,380,440,45.05K, 120,160,280,360,480,45.02K, 120,320,340,340,550,44.71K, 160,400,280,400,495,44.8K, 0,0,0,0,0,47.1K, 0,0,0,40,1810,45.47K, 0,200,20,640,3560,44.39K, 0,880,0,2340,5825,41.97K, 40,7360,280,6440,8685,35.07K, 120,16.88K,1100,8200,9665,28.34K, 240,18.88K,1560,8660,10.15K,25.98K, 120,22.16K,2380,8860,10.75K,23.32K, 120,25.36K,2140,9820,10.48K,20.35K, 440,29K,3300,10.64K,11.46K,16.36K, 680,36.08K,4740,10.9K,11.31K,11.62K, 1480,43.52K,6600,11.44K,11.44K,4760, 1880,48.2K,8240,11.8K,11.7K,0, 0,0,0,120,1200,45.12K, 0,120,0,640,2670,44.76K, 0,640,0,2080,4235,43.13K, 40,5640,220,4360,6340,37.33K, 80,11.84K,880,6140,7670,31.5K, 40,14.32K,1220,6660,7725,30.59K, 160,16.64K,1540,7060,7520,29.87K, 360,19.32K,2020,7040,7935,26.74K, 480,23.72K,2920,7560,8385,23.44K, 600,26.28K,3820,8460,8270,19.66K, 1160,33.04K,5600,8360,8545,14.58K, 1720,36.28K,6380,8760,8780,10.82K, 0,0,0,100,1265,45.43K, 0,120,0,400,2560,45.8K, 0,560,20,1740,3875,43.54K, 0,5520,300,3740,5960,39.37K, 80,10.56K,1080,5560,6645,33.64K, 80,13.2K,1260,5680,7055,32.09K, 240,15.08K,1680,5620,7100,30.85K, 200,16.12K,2000,6740,7350,28.75K, 320,20.64K,3100,6780,7245,25.65K, 920,23.44K,3860,7020,7505,22.32K, 1280,28.04K,5820,7940,7885,17.34K, 1400,31.84K,6940,8120,8050,14.14K, 0,0,0,100,750,45.85K, 0,40,0,400,1750,45.5K, 0,840,0,1080,2600,43.39K, 0,4120,240,2980,3885,41.44K, 0,8200,840,4100,4345,38.15K, 160,8680,920,4180,4530,36.78K, 80,10.88K,1100,4140,4625,36.52K, 240,11.48K,1360,4780,4630,34.1K, 400,14.48K,1900,5160,4930,31.98K, 520,17.04K,2580,4920,4580,30.03K, 960,20.16K,4060,5600,5065,26.35K, 1160,22.92K,4620,5360,5085,24.67K, 0,0,0,20,190,46.29K, 0,40,0,60,470,47.06K, 0,80,0,300,795,46.04K, 0,840,80,920,1145,44.79K, 0,2120,240,820,1275,43.99K, 0,2360,360,1000,1415,43.64K, 40,2520,300,1180,1550,42.89K, 40,2760,360,1360,1395,43.44K, 40,3320,680,1320,1550,42.93K, 120,3600,1000,1300,1665,41.55K, 280,4160,1400,1620,1635,40.62K, 360,4760,1540,1700,1715,40.51K, 0,0,0,0,155,46.57K, 0,0,0,20,255,46.43K, 0,0,20,180,460,47.19K, 0,360,0,420,660,45.95K, 0,800,140,640,690,45.77K, 0,1080,280,560,835,45.32K, 40,1080,200,700,810,44.79K, 160,1160,300,840,950,45.28K, 40,1560,420,680,945,44.39K, 120,1480,540,840,970,44.52K, 320,1760,960,880,1100,42.87K, 360,2360,1100,780,1075,42.55K, 0,0,0,0,50,45.92K, 0,0,0,20,125,46.27K, 0,0,0,60,185,47.58K, 0,0,20,200,315,45.75K, 0,120,60,220,410,45.97K, 0,80,180,200,380,47.02K, 0,120,100,280,415,45.59K, 0,240,200,240,465,45.89K, 40,360,160,300,410,45.32K, 160,200,280,360,485,45.72K, 120,280,260,380,585,44.65K, 240,480,400,360,525,46.04K, 0,0,0,0,0,49.7K, 0,0,0,80,1825,47.62K, 0,120,0,680,3755,46.05K, 0,1360,20,2180,5855,43.88K, 40,8120,380,5940,8775,37.2K, 40,16.88K,1280,7680,10.24K,29.41K, 80,19.48K,1760,8780,9880,26.91K, 160,22.64K,2180,8780,10.97K,25.06K, 280,25.4K,2420,9780,10.66K,21.59K, 440,31.48K,3960,10.54K,11.02K,17.12K, 440,37.08K,5520,11.08K,11.34K,12.26K, 1720,45.08K,6760,11.4K,11.9K,4765, 2440,48.96K,9380,12.18K,12.06K,0, 0,0,0,120,1410,47.79K, 0,40,0,660,2825,47.03K, 0,840,0,1880,4405,44.87K, 40,5640,400,4740,6505,39.62K, 40,13.72K,1160,5760,7500,33.89K, 120,15.16K,1180,6360,7815,32.15K, 120,16.92K,1800,7300,7840,30.18K, 120,19.88K,2240,7240,8050,28.4K, 440,24.6K,3080,7560,8225,24.86K, 760,27.36K,4560,7820,8355,20.52K, 1560,33.6K,6040,8420,8755,14.69K, 1520,37.4K,7360,8920,8770,11.42K, 0,0,0,60,1315,48.53K, 0,80,0,540,2475,47.76K, 0,960,20,1840,3970,46.08K, 0,5080,420,4020,5810,41.2K, 40,11.52K,1160,5660,6950,34.63K, 120,13.32K,1360,5960,6955,33.41K, 240,14.96K,1760,6600,7080,32.56K, 360,17.12K,2060,6600,7285,30.55K, 600,20.92K,2920,6720,7415,27.7K, 680,23.84K,4240,7760,7710,23.14K, 1480,29.08K,5900,7880,7630,17.93K, 1640,33.12K,7040,8160,8185,15.38K, 0,0,0,40,870,48.95K, 0,160,0,400,1780,47.62K, 0,640,20,1020,2795,46.35K, 40,3680,240,3120,4065,44.26K, 40,8400,840,4280,4415,39.56K, 120,8880,1040,4620,4540,38.88K, 360,10.96K,1440,4580,4580,37.69K, 120,12.2K,1560,4460,4830,35.58K, 400,15.24K,2100,5160,4950,34.41K, 640,17.4K,3280,4920,4785,31.26K, 920,21.72K,4320,5240,5310,27.57K, 1240,23.48K,5120,5100,5270,26.03K, 0,0,0,40,185,48.88K, 0,0,0,160,585,48.58K, 0,80,0,340,855,48.79K, 0,1000,60,780,1240,47.59K, 0,1960,200,1280,1310,45.34K, 40,2080,260,1320,1415,46.09K, 40,2680,340,1340,1430,44.72K, 40,3160,600,1200,1475,45.59K, 120,3280,720,1540,1640,45.49K, 80,3920,1060,1700,1495,44.2K, 520,4160,1400,1740,1650,43.17K, 520,5080,1800,1560,1620,42.58K, 0,0,0,0,170,48.34K, 0,0,0,40,345,48.54K, 0,40,0,200,485,49.06K, 0,320,0,440,740,48.59K, 0,880,180,580,820,47.9K, 0,920,300,660,910,47.69K, 0,1120,260,680,870,47.38K, 80,1240,320,700,940,47.36K, 120,1440,360,740,970,47.81K, 240,1640,700,740,1010,46.65K, 240,1880,940,780,1150,46.09K, 440,2200,960,780,1165,46.11K, 0,0,0,0,55,48.64K, 0,0,0,0,135,48.11K, 0,0,0,0,220,49.51K, 0,0,60,180,340,48.33K, 0,80,80,280,350,49.14K, 0,40,40,240,415,48.05K, 0,240,120,260,390,47.76K, 40,120,140,360,440,48.05K, 160,280,100,260,455,47.39K, 160,160,300,360,505,48.54K, 120,240,340,520,515,47.45K, 360,360,420,480,460,47.29K, 0,0,0,0,0,53.01K, 0,0,0,180,1885,51.25K, 0,280,0,900,3745,50.15K, 0,1360,40,2780,5925,47.24K, 0,9320,460,6360,8840,39.1K, 40,18.88K,1840,8260,10.09K,32.17K, 280,21.04K,1980,8800,10.38K,29.31K, 200,23.36K,2480,9340,10.73K,26.41K, 640,27.2K,3800,10.18K,10.84K,23.83K, 440,32.88K,4940,10.5K,11.37K,18.23K, 1240,38.32K,5880,11.26K,11.5K,13.06K, 1600,47.08K,8140,11.46K,11.8K,5145, 2640,53.08K,10.24K,11.98K,11.94K,0, 0,0,0,160,1505,52.08K, 0,200,0,640,2835,51.04K, 0,800,20,2420,4360,49.51K, 0,6160,340,5000,6810,42.36K, 80,14.8K,1180,6580,7165,36.79K, 200,16.52K,1640,6160,7810,34.83K, 240,18.76K,2200,6900,8155,33.14K, 480,20.12K,2520,7600,8150,29.99K, 560,25.92K,4180,7420,8315,26.46K, 880,28.84K,5200,8820,8470,22.48K, 1600,35.12K,6820,9180,8805,15.71K, 2440,38.88K,8240,8960,8795,12.04K, 0,0,0,60,1315,52.28K, 0,280,0,540,2590,50.04K, 0,840,0,2060,4200,48.76K, 0,6040,220,4340,6075,42.8K, 80,11.96K,1220,5580,7070,37.83K, 120,14.36K,1560,5940,6980,36.67K, 360,16.48K,2220,6380,7080,34.92K, 320,18.4K,2540,6520,7305,32.66K, 440,21.44K,3740,7360,7465,29.38K, 960,25.92K,5140,7400,7455,25.54K, 1640,30.76K,6300,8000,7730,19.73K, 2560,34.28K,8180,8140,8135,16.89K, 0,0,0,80,870,52.72K, 0,160,0,400,1870,51.05K, 0,760,20,920,2930,50.94K, 40,4960,220,3320,3610,46.13K, 200,8920,900,3900,4380,41.96K, 80,10.36K,1200,4720,4685,41.09K, 160,10.88K,1460,4600,5005,39.11K, 360,13.36K,2140,4640,4715,39.37K, 280,16.44K,2900,5060,5030,36.99K, 480,17.72K,3580,5440,5295,33.08K, 1600,21.76K,4700,5660,5245,29.99K, 1880,24.32K,5660,5420,5305,26.87K, 0,0,0,20,220,52.49K, 0,40,0,180,530,52.41K, 0,80,0,320,845,51.92K, 0,880,80,880,1230,51.17K, 0,2240,320,1240,1485,49.18K, 80,2440,420,1120,1525,49.77K, 120,2960,420,1200,1410,49.75K, 120,2880,520,1380,1495,48.72K, 160,3760,1000,1360,1470,47.27K, 360,4040,1280,1720,1480,47.77K, 600,4320,1920,1720,1720,46.37K, 640,5000,2100,1780,1660,45.44K, 0,0,0,0,160,52.29K, 0,0,0,20,260,52.44K, 0,80,0,180,470,52.77K, 0,480,100,400,720,52.12K, 0,920,260,580,830,50.57K, 40,1200,400,700,810,51.14K, 40,1360,400,680,765,50.97K, 40,1480,420,640,920,49.5K, 40,1600,620,660,915,49.7K, 240,1720,800,820,1010,49.36K, 520,1920,1120,800,1115,48.14K, 440,2320,1140,780,1140,48.52K, 0,0,0,0,55,52.89K, 0,0,0,0,100,51.59K, 0,0,20,60,195,52.22K, 0,40,40,200,340,51.96K, 0,160,80,200,470,52.28K, 80,160,100,240,415,53.49K, 0,200,100,260,400,52.05K, 40,280,200,280,400,51.54K, 80,240,360,340,485,51.7K, 120,360,420,380,405,51.63K, 200,480,300,340,500,51.83K, 200,280,520,380,565,50.95K, 0,0,0,0,0,56.46K, 0,0,0,200,1915,56.41K, 0,360,0,980,4160,53.53K, 0,1680,20,2840,6045,50.5K, 0,9720,520,6240,9280,42.52K, 240,19.92K,1700,9240,10.86K,33.4K, 320,23.12K,2780,9160,10.29K,31.08K, 160,26.32K,2940,9860,10.96K,28.18K, 640,29.4K,3900,10.16K,10.82K,25.19K, 920,36.56K,5040,10.76K,11.2K,19.82K, 1680,41.24K,6620,11.74K,11.67K,14.25K, 2800,50.36K,9240,11.56K,12.17K,5430, 2960,55.4K,11.8K,12.02K,12.19K,0, 0,0,0,140,1455,56.36K, 0,200,20,620,3135,54.71K, 0,1400,40,2080,4705,52.43K, 0,7360,520,5240,6795,45.27K, 40,15.68K,1560,6440,7540,38.84K, 240,17.76K,2160,7220,7660,37.93K, 480,20.4K,2400,7480,8190,33.84K, 480,22.56K,3240,7840,8040,32.67K, 680,26.72K,4340,8080,8220,28.5K, 1240,32.32K,6040,7880,8400,24.57K, 2120,38.12K,7820,8540,9010,16.95K, 2720,41.36K,9420,8840,9125,12.97K, 0,0,0,120,1345,56.11K, 0,160,0,700,2925,53.49K, 0,1040,40,1740,4405,52.31K, 0,6280,500,5060,5900,46.02K, 80,13.36K,1560,5880,7065,40.94K, 120,15.16K,2200,6280,7135,39.36K, 280,17.68K,2800,6980,7115,37.06K, 440,19.12K,3220,6720,7840,35.43K, 840,23.48K,3780,6900,8090,30.89K, 1280,28.76K,6000,7120,7855,27.14K, 2160,33.52K,7040,8200,7670,21.1K, 2400,35.96K,9080,8860,8370,17.04K, 0,0,0,100,1035,55.63K, 0,200,0,300,1945,55.89K, 0,760,0,1440,2915,53.41K, 40,5280,280,3300,4065,50.34K, 40,10K,840,4240,4440,45.53K, 200,11.32K,1420,4780,4480,44.57K, 320,13.16K,1580,5080,4815,43.19K, 400,14.28K,2380,4680,4880,41.73K, 640,16.96K,3040,5300,4895,38.79K, 920,19.28K,4440,5500,5110,36.04K, 1560,22.88K,5300,5700,5355,32.3K, 2240,25.92K,6280,5420,5125,30.24K, 0,0,0,0,310,56K, 0,0,0,160,495,55.9K, 0,120,0,360,785,56.08K, 0,1160,120,820,1260,54.28K, 40,2440,380,1340,1465,53.87K, 0,2600,520,1000,1510,52.9K, 80,3040,540,1340,1565,53.25K, 120,3000,720,1480,1500,52.73K, 240,3840,1240,1480,1520,51.53K, 320,4600,1440,1340,1520,51.13K, 440,4760,2020,1460,1775,49.43K, 680,5560,2180,1500,1765,48.96K, 0,0,0,20,120,55.98K, 0,0,0,40,270,56.33K, 0,120,20,120,500,56.76K, 0,520,140,460,790,55.74K, 40,1080,320,600,755,54.5K, 80,1240,340,760,865,54.73K, 0,1280,500,720,960,54.35K, 120,1400,620,720,990,54.08K, 40,1600,860,760,960,53.84K, 280,2120,940,700,1025,52.95K, 480,2160,1060,900,1070,52.84K, 400,2280,1660,1000,1120,52.3K, 0,0,0,0,55,56.47K, 0,0,0,20,95,57.76K, 0,0,0,40,240,56.11K, 0,40,40,200,400,56.97K, 40,120,100,340,400,56.94K, 0,160,160,300,435,56.42K, 40,200,340,260,440,55.81K, 40,280,240,300,510,55.11K, 80,320,260,420,440,56.09K, 40,400,360,380,450,55.7K, 120,480,540,380,500,55.91K, 240,400,640,340,545,54.39K, 0,0,0,0,0,60.58K, 0,0,0,220,2195,58.9K, 0,200,0,1160,4435,57.66K, 40,1880,40,3020,6330,54.58K, 40,11.16K,620,6540,9380,45.57K, 80,21.96K,2200,8960,10.61K,36.4K, 160,24.8K,2640,10.1K,10.5K,33.47K, 240,28.88K,3980,10.2K,10.75K,30.59K, 760,31.8K,4180,10.3K,11.48K,27.48K, 1440,38.12K,6020,11.26K,11.51K,21.2K, 1840,45.16K,7940,11.5K,12.04K,15.83K, 3400,55.12K,10.96K,11.78K,11.86K,5740, 4200,58.56K,12.96K,12.88K,12.31K,0, 0,0,0,80,1445,59.85K, 0,160,0,820,3250,58.01K, 0,1160,60,2240,5055,55.99K, 40,8160,480,5480,6720,48.38K, 160,16.52K,1740,6980,7590,41.61K, 400,19.16K,2480,7360,7990,40.01K, 320,22.92K,2980,7720,7945,37.75K, 360,24.04K,3540,8420,8190,35.21K, 840,29.88K,5100,8580,8220,30.59K, 1640,33.84K,6360,8460,8685,25.9K, 2240,39.88K,8620,9240,8745,18.81K, 3800,43.04K,10.32K,9460,8955,14.13K, 0,0,0,60,1560,59.18K, 0,280,0,700,2900,58.78K, 0,1440,80,2080,4510,56.57K, 0,7800,460,4820,6255,49.53K, 160,14.36K,1660,6520,7035,44.2K, 280,16.96K,2360,6820,7290,41.96K, 360,19.6K,2720,6200,7290,40.27K, 440,21.32K,3500,7220,7555,37.08K, 720,26.08K,4780,7040,7965,33.19K, 1480,29.88K,6080,8180,7770,28.61K, 2560,34.88K,7900,7900,8335,23K, 3240,39.52K,10.16K,8440,7800,18.48K, 0,40,0,160,1110,60.32K, 0,160,40,680,1945,59.96K, 0,1160,40,1380,3105,57.08K, 0,5600,260,3600,3890,53.27K, 120,10.96K,1080,4540,4500,49.23K, 280,12.48K,1420,4600,4755,48.23K, 280,15K,2000,4640,4745,45.83K, 360,15.36K,2860,4880,4865,44.91K, 880,18.64K,3180,5320,4830,41.87K, 1400,21.16K,4160,5200,5150,38.1K, 1800,24.28K,6440,5720,5335,34.1K, 2400,26.8K,7480,6000,5185,31.72K, 0,0,0,20,325,61.09K, 0,40,0,60,625,59.42K, 0,160,0,420,970,59.61K, 0,1160,220,960,1305,58.69K, 40,2720,360,1140,1320,57.76K, 120,2800,640,1140,1625,57.35K, 80,3320,740,1320,1565,56.29K, 120,3600,1120,1280,1530,56.57K, 160,4000,1700,1320,1555,55.13K, 520,4720,1720,1560,1440,55.22K, 440,4760,2380,1760,1670,52.24K, 720,5720,2620,1580,1770,53.26K, 0,0,0,0,170,60.26K, 0,0,0,60,295,60.64K, 0,120,0,180,520,60.31K, 0,440,80,580,740,59.82K, 0,1120,380,680,850,59.09K, 0,1120,540,740,905,58.66K, 80,1680,440,680,1060,58.22K, 120,1800,560,600,940,57.83K, 160,1880,820,740,940,57.67K, 280,2400,1100,580,1155,57.19K, 440,2440,1140,900,1120,57K, 800,2520,1540,940,975,55.96K, 0,0,0,0,45,60.24K, 0,0,0,0,120,60.53K, 0,0,40,40,260,59.55K, 0,120,60,180,385,60.17K, 0,120,180,320,440,60.36K, 0,160,220,300,500,59.6K, 80,240,280,320,455,59.63K, 40,240,260,300,465,58.95K, 160,200,300,420,540,59.24K, 280,320,460,360,510,59.92K, 120,280,660,480,565,59.07K, 200,560,540,400,555,58.11K, 0,0,0,0,0,62.93K, 0,0,0,240,2255,62.49K, 0,240,0,1340,4480,59.81K, 0,2280,40,3460,6490,57.36K, 0,11.56K,640,7580,9315,48.1K, 320,23.96K,3000,9320,10.59K,38K, 280,27.68K,3120,10.1K,10.71K,35.53K, 1000,31.04K,3820,10.64K,11.71K,31.74K, 1040,34.48K,4640,10.58K,10.94K,29.12K, 1520,41.4K,6200,11.22K,11.85K,22K, 2200,46.76K,8300,12.24K,12.17K,15.93K, 3120,56.76K,11.86K,12.64K,11.88K,6455, 4840,61.48K,14.62K,13.38K,12.25K,0, 0,0,0,140,1590,62.8K, 0,200,0,860,3325,61.8K, 0,1480,40,2400,4915,57.89K, 120,8960,460,5620,7195,51.67K, 240,17.88K,2100,7380,7655,44.86K, 320,21K,2820,7760,8200,42.09K, 440,24.24K,3060,7760,7830,39.15K, 880,26.52K,3520,7780,8170,36.88K, 1080,30.76K,5760,8820,8225,31.23K, 1880,35.72K,7100,8980,8435,26.84K, 3120,43.2K,9180,9580,8915,19.97K, 3920,45.44K,11.4K,9340,9345,14.62K, 0,0,0,180,1505,63.03K, 0,200,0,720,2940,61.86K, 0,1320,60,2020,4890,58.18K, 0,8720,480,4940,6465,53.18K, 200,16K,2160,6080,7275,46.34K, 320,18.08K,2300,7100,7460,43.94K, 440,21K,2920,6740,7485,41.92K, 720,23.76K,3740,7020,7810,38.91K, 1200,27.52K,5560,7540,7590,34.71K, 1720,32.52K,6520,7540,7855,30.11K, 2720,36.32K,9000,8460,7895,24.04K, 4200,40.52K,11.02K,8420,8215,20.16K, 0,40,0,120,995,63.01K, 0,200,0,580,2015,62.72K, 0,880,120,1600,3265,61.11K, 40,6240,500,3440,4185,56.11K, 120,11.68K,1460,4540,4650,52.15K, 360,13K,2000,4660,4650,49.71K, 400,14.8K,2420,4380,5220,48.42K, 640,16.88K,2600,4800,4665,48.01K, 840,20.2K,3460,4920,5030,44.51K, 1400,21.92K,4940,5220,5230,40.79K, 1920,25.72K,6620,5820,5350,36.1K, 3280,27.2K,8140,6360,5425,34.06K, 0,0,0,20,285,63.93K, 0,0,0,120,695,62.33K, 0,280,60,440,850,63.09K, 40,1400,100,760,1430,61.55K, 40,2440,620,1520,1525,60.77K, 160,3120,600,1140,1585,60.08K, 40,3680,860,1260,1605,59.88K, 0,3360,1140,1320,1745,58.87K, 280,4120,1680,1380,1720,58.2K, 360,4720,1920,1580,1770,58.01K, 1000,4960,2340,1700,1715,55.75K, 960,5960,2680,1700,1740,55.48K, 0,0,0,0,190,63.02K, 0,0,0,40,350,63.08K, 0,120,40,220,485,63.24K, 0,720,60,480,735,62.73K, 80,1280,440,540,875,61.2K, 80,1360,440,640,930,62.05K, 120,1800,480,780,890,61.7K, 120,1800,640,740,945,61.34K, 40,1840,840,820,1045,60.27K, 400,2360,980,880,1080,60.72K, 320,2680,1360,1060,1070,59.78K, 920,2160,1500,1100,1230,59.16K, 0,0,0,0,45,63.74K, 0,0,20,20,135,64.17K, 0,0,40,60,165,63.23K, 0,80,20,180,405,62.85K, 40,160,100,280,485,63.16K, 0,200,160,260,505,62.67K, 40,240,220,360,435,62.95K, 80,280,260,300,485,63.59K, 120,200,300,480,590,62.96K, 280,480,360,420,435,62.65K, 240,480,660,260,535,62.77K, 280,440,540,500,650,62.08K, 0,0,0,0,0,66.44K, 0,40,0,220,2340,64.91K, 0,600,0,1060,4535,62.73K, 0,2520,80,3380,6685,58.96K, 0,12.4K,940,7280,9580,49.68K, 160,24.92K,2660,9640,10.89K,40.13K, 200,29.56K,3260,10.06K,10.86K,36.67K, 760,32.64K,4060,11.12K,11.14K,33.18K, 800,36.88K,4820,10.48K,11.04K,30.08K, 1560,43.48K,7600,11.68K,11.52K,23.74K, 2160,49.96K,8920,11.9K,11.79K,16.06K, 3640,59.36K,12.92K,12.36K,12.26K,6650, 5680,64.4K,15.74K,12.48K,11.97K,0, 0,0,0,120,1925,65.07K, 40,200,0,840,3435,63.62K, 0,1920,20,2520,5240,61.44K, 0,10.08K,540,5320,7095,53.27K, 280,18.72K,2180,7420,7945,45.73K, 440,22.36K,2940,7580,8150,44.02K, 600,24.24K,3760,7780,8585,39.83K, 640,27.48K,4720,8500,8330,38.33K, 1240,32.12K,5760,8660,8725,33.05K, 2240,37.88K,7260,8680,8765,27.36K, 3760,45K,9860,9600,9030,20.33K, 4800,47.12K,12K,9400,9290,15.36K, 0,40,0,100,1485,65.36K, 0,160,0,880,3145,62.45K, 0,1280,60,2660,4790,62.11K, 0,9280,520,5060,6510,54.4K, 440,16.56K,2320,6780,7325,47.76K, 320,19.44K,2820,7020,7280,45.62K, 360,21.36K,3420,7240,7280,43.21K, 920,24.4K,4360,7360,7525,40.43K, 1080,28.44K,5680,8140,7765,36.44K, 1640,32.36K,7180,8560,7970,31.85K, 3160,37.56K,9360,8320,8045,24.54K, 4440,41.48K,10.76K,8420,8200,20.3K, 0,40,0,100,1120,65.62K, 0,320,0,540,1940,64.31K, 0,1200,60,1660,3000,63.46K, 0,6320,620,3840,4175,57.04K, 160,11.8K,1460,4580,4815,53.06K, 240,14.16K,2020,4880,4685,52K, 360,15.68K,2520,4640,4835,50.54K, 640,17.16K,2920,5140,5065,48.03K, 920,20.84K,4180,5340,5165,44.96K, 1360,22.44K,5660,5660,5565,42.1K, 2400,26.96K,7020,5440,5190,38.08K, 3080,28.96K,7840,5780,5130,35.21K, 0,0,0,0,275,66.26K, 0,40,0,160,695,65.35K, 0,160,20,460,925,64.5K, 0,1520,100,800,1315,63.95K, 40,2680,580,1180,1635,61.67K, 200,3200,780,1160,1555,63.4K, 160,3960,1020,1300,1755,61.98K, 200,3560,1180,1460,1530,61.43K, 320,4160,1640,1640,1585,60.21K, 400,5000,1960,1660,1720,58.74K, 1000,5320,2300,1580,1775,58.21K, 960,5920,2660,1740,1785,56.68K, 0,0,0,0,180,66.09K, 0,0,0,40,345,65.53K, 0,80,0,260,525,65.77K, 0,640,160,540,810,65.22K, 0,1200,340,700,860,63.08K, 120,1360,500,900,925,62.99K, 80,1480,600,740,990,63.14K, 160,1640,640,900,1080,63.72K, 200,2000,720,880,1035,62.65K, 480,2200,1320,880,1015,62.74K, 640,2880,1460,900,1010,61.8K, 960,2120,1840,1060,1155,61.24K, 0,0,0,0,45,65.14K, 0,0,0,0,160,65.56K, 0,0,0,60,190,64.83K, 0,0,80,260,415,64.85K, 40,240,180,320,405,64.64K, 0,160,300,320,450,66.06K, 80,240,340,340,395,65.61K, 80,400,260,300,415,65.14K, 160,360,340,460,490,65.08K, 200,480,420,380,515,64.56K, 360,240,440,420,575,64.37K, 360,520,560,440,535,64.78K, 0,0,0,0,0,68.05K, 0,0,0,260,2460,66.57K, 0,280,0,1460,4790,64.8K, 0,2120,20,4100,6910,62.11K, 0,13K,900,7980,9505,51.28K, 160,26K,2700,9700,10.78K,41.03K, 360,30.36K,4100,10.04K,10.74K,37.15K, 600,34.84K,4600,11.02K,11.35K,33.85K, 880,38.24K,5640,10.84K,11.89K,30.6K, 1360,45.12K,7540,11.44K,11.83K,23.73K, 2400,50.84K,10.92K,12.72K,12.08K,17.41K, 4240,59.64K,14.08K,12.98K,12.09K,6830, 5680,65.68K,15.78K,13.38K,12.12K,0, 0,40,0,180,1795,66.91K, 0,320,0,920,3550,65.88K, 0,1920,60,2560,5180,63.29K, 80,10.52K,900,5680,7320,54.18K, 240,19.92K,2240,7760,7915,47.17K, 360,24K,2920,7540,7815,43.82K, 680,25.88K,3980,8020,8240,42.57K, 640,28.64K,4500,7920,8220,39.81K, 1480,33.52K,6260,8440,8370,34.1K, 2280,38.56K,8640,9160,8960,28.92K, 3240,46.36K,11.18K,9460,8750,20.61K, 4560,48.36K,12.66K,10K,9015,15.82K, 0,0,0,180,1560,68.11K, 0,120,0,960,3140,66.71K, 0,1760,60,2760,4600,63.78K, 40,9160,760,5400,6390,55.8K, 400,17.68K,2360,6760,7060,49.52K, 400,19.92K,2720,7140,7460,47.14K, 520,22.4K,3960,7620,7615,44.77K, 800,25.6K,4320,7360,7755,42.92K, 1360,29.48K,5920,8020,7755,37.12K, 2360,34.08K,7440,8100,8215,33.23K, 3520,39.2K,10.18K,8320,8350,25.46K, 4520,42.4K,11.8K,8520,8260,20.84K, 0,0,20,160,1175,67.72K, 0,360,0,660,2050,66.45K, 0,1200,60,1820,3170,64.11K, 0,6560,560,3600,4395,59.07K, 120,11.8K,1860,4880,4970,54.98K, 520,14.6K,2060,5040,4870,52.7K, 600,16.16K,2600,5120,4995,51.51K, 640,17.88K,3060,5200,5140,49.86K, 1040,20.8K,4180,5580,5205,46.52K, 1520,23.68K,5160,6000,5105,44K, 2680,27.32K,7480,5600,5235,39.03K, 3160,29.72K,8940,5780,5575,34.97K, 0,0,0,40,300,67.17K, 0,40,0,140,645,67.12K, 0,160,20,360,1120,65.8K, 0,1400,240,1160,1380,65.88K, 0,3120,520,1160,1435,64.44K, 40,3200,900,1380,1690,64.13K, 160,3840,820,1620,1615,64.02K, 120,4240,1020,1360,1560,62.79K, 400,4600,1500,1520,1505,61.5K, 520,4960,1760,1780,1630,61.02K, 880,5640,2360,1660,1845,59.48K, 1320,6280,3060,1580,1870,58.59K, 0,0,0,20,185,67.9K, 0,0,0,40,395,67.8K, 0,120,20,180,570,67.17K, 0,520,180,620,805,67.06K, 80,1480,340,720,940,65.86K, 0,1640,340,700,975,65.62K, 120,1480,680,740,940,64.56K, 40,1760,800,740,1035,65.96K, 200,2040,1020,900,1030,65.17K, 320,2240,1160,820,1055,64.57K, 800,2320,1360,1000,1125,62.87K, 840,2520,1740,960,1200,61.99K, 0,0,0,0,40,67.43K, 0,0,0,0,150,67.61K, 0,0,0,80,225,68.49K, 0,80,60,220,385,67.3K, 0,120,180,340,565,67.27K, 80,240,160,260,480,66.4K, 120,200,220,400,510,67.53K, 80,160,280,420,520,66.25K, 160,200,520,400,600,64.61K, 240,440,320,420,595,66.19K, 360,480,620,400,485,67.25K, 480,400,720,460,410,66.56K, 0,0,0,0,0,71.1K, 0,0,0,200,2480,70.23K, 0,480,0,1340,5000,67.85K, 0,2800,120,3860,6895,63.74K, 40,14.68K,1120,7380,9975,53.27K, 280,26.84K,2900,10.6K,10.87K,42.96K, 440,31.52K,4120,10.78K,11.07K,39.31K, 800,35.2K,5100,11.46K,11.29K,35.58K, 1080,40.4K,6180,11.08K,11.24K,31.42K, 2160,46.4K,8340,11.64K,12.48K,24.78K, 2640,53.32K,10.84K,11.88K,12K,17.22K, 5560,63.44K,13.94K,12.08K,12.55K,7620, 6720,66.24K,16.8K,13.28K,12.86K,0, 0,0,0,180,1935,70.16K, 0,440,0,1060,3660,68.69K, 0,1960,100,2820,5260,65.88K, 80,11.24K,820,5980,7105,57.53K, 280,21.4K,2440,7060,7575,49.85K, 440,25.36K,3040,7620,7805,46.42K, 640,27.12K,4400,8460,8205,43.92K, 840,29.48K,4860,8280,8355,40.87K, 1680,34.08K,6980,9080,8880,35.84K, 2680,40.56K,8080,8700,8900,30.35K, 4040,48.48K,12.08K,8820,9070,22.16K, 5240,49.92K,13.66K,9740,9235,16.14K, 0,0,0,220,1550,69.22K, 0,280,40,1040,3315,68.05K, 0,1800,40,2960,4445,66.39K, 40,9440,680,5400,6225,58.09K, 240,18.8K,2360,6400,7290,51.14K, 240,20.64K,3140,7180,7445,48.68K, 680,23.72K,3740,7100,7450,47.34K, 1200,26.32K,4320,8080,7380,44.15K, 1760,30K,6100,8400,7755,38.79K, 2240,35.8K,7820,7860,7900,34.15K, 4280,39.96K,11.32K,8660,8565,27K, 5200,43.6K,12.22K,8760,8320,21.86K, 0,0,0,140,1210,70.36K, 0,320,20,700,2190,69.82K, 0,1360,60,1640,3390,68.15K, 40,6720,680,4040,4365,63.47K, 360,13.72K,1640,4620,4710,57.17K, 360,15.4K,2320,4800,4885,56.47K, 520,16.48K,2880,4980,4930,53.94K, 800,18.68K,3500,5500,4915,51.84K, 1200,21.92K,4420,5460,5020,48.98K, 1920,25K,6160,5700,5130,44.94K, 3480,27.6K,8120,5940,5480,40.25K, 4120,30.12K,9200,5600,5590,37.43K, 0,0,0,40,315,71.31K, 0,80,0,200,590,70.83K, 0,120,0,560,975,71.31K, 0,1680,160,1020,1445,68.67K, 80,3080,740,1180,1575,67.28K, 200,3680,680,1140,1610,66.58K, 160,4120,940,1320,1605,65.4K, 240,4240,1100,1480,1555,64.79K, 360,4600,1760,1580,1530,65.09K, 680,4880,1940,1740,1690,63.27K, 1040,5520,3040,1900,1790,62.61K, 1480,5920,3000,1760,1850,61.62K, 0,0,0,20,220,70.57K, 0,0,0,20,410,70.88K, 0,0,0,260,520,71.02K, 40,840,200,460,795,69.49K, 0,1560,580,640,910,68.79K, 200,1760,620,560,1160,68.1K, 120,1760,560,700,955,68.65K, 80,1800,960,880,1060,67.92K, 280,2040,1160,880,1045,67.77K, 320,2360,1280,940,1105,67.05K, 560,2480,1820,960,1125,66.01K, 960,2720,1360,960,1240,66.45K, 0,0,0,0,70,70.96K, 0,0,0,20,185,70.82K, 0,0,0,80,290,70.08K, 0,120,120,220,315,70.51K, 40,320,160,280,445,70.99K, 80,280,280,220,485,70.22K, 0,240,320,400,510,69.5K, 120,280,300,480,420,70.5K, 160,360,380,400,505,70.18K, 280,440,460,500,520,69.51K, 560,480,540,340,515,69.82K, 400,520,880,420,495,68.3K, 0,0,0,0,0,76.18K, 0,40,0,240,2610,74.43K, 0,640,40,2020,4760,72.43K, 0,2800,60,4160,7040,68.87K, 160,16.16K,1420,8080,9840,57.09K, 360,29.04K,3520,10.46K,11.11K,45.56K, 840,33.6K,4580,10.66K,11.22K,42.26K, 1120,38.08K,5520,10.84K,11.75K,37.98K, 1240,42.92K,6720,11.2K,11.44K,34.36K, 2720,48.8K,9580,11.96K,12K,26.7K, 3560,57.28K,11.28K,12.44K,12.21K,19.33K, 5720,65.4K,15.86K,13.66K,12.57K,7890, 8440,70.04K,17.98K,13.58K,12.94K,0, 0,120,0,220,2000,75.32K, 0,400,0,1200,3850,72.72K, 0,2720,60,3080,5245,70.35K, 120,12.32K,940,6160,7180,61.13K, 320,22.68K,2980,7600,8010,52.54K, 560,26.44K,4020,7840,8345,50.01K, 840,29.16K,5080,7980,8190,47.17K, 1240,32.08K,5620,8280,8350,44.66K, 1840,37.24K,7640,9200,8765,38.06K, 2640,42.36K,9680,9680,8900,31.93K, 4640,49.88K,12.66K,9820,8925,23.52K, 6800,52.36K,15.18K,9720,9435,17.63K, 0,0,0,120,1855,75.42K, 0,360,0,1040,3295,73.76K, 0,2280,60,2740,4710,71.5K, 80,10.24K,680,5500,6950,63.2K, 320,19.8K,2640,6600,7460,54.71K, 720,22.68K,3780,6880,7460,52.8K, 880,24.92K,4480,7380,7730,50.16K, 1200,27.52K,5620,7780,7790,47.42K, 2760,31.88K,7280,7680,7740,42.09K, 2600,36.16K,9160,8620,7755,37.13K, 4680,42K,11.88K,8860,8355,28.79K, 6440,45.2K,13.64K,9360,8245,23.45K, 0,40,0,80,1135,75.57K, 0,360,40,840,2425,74.73K, 0,1520,20,2080,3335,73.03K, 40,7680,820,4000,4620,66.54K, 200,14.6K,2180,5100,4535,61.51K, 760,16.24K,2620,4840,4880,60.61K, 560,18K,3300,4960,5020,58.44K, 1120,20K,4040,5260,5065,56.41K, 1640,22.88K,5200,5200,5365,52.89K, 1760,25.56K,6860,5840,5025,49.1K, 3760,28.92K,9120,5460,5655,42.35K, 4520,30.92K,10.18K,6340,5450,39.8K, 0,0,0,0,395,77.17K, 0,80,0,220,640,75.58K, 0,400,20,420,890,76.47K, 40,1800,220,960,1480,73.04K, 80,3160,440,1260,1410,71.61K, 160,3400,1120,1680,1545,70.16K, 280,4040,1180,1360,1775,71.27K, 280,4480,1280,1400,1735,70.29K, 440,4800,2080,1600,1610,69.67K, 920,5200,2240,1620,1690,68.9K, 1080,6000,3280,1620,1795,67.8K, 1360,6000,3920,1840,1710,66.83K, 0,0,0,40,220,76.97K, 0,0,0,80,455,75.73K, 0,40,0,360,515,76.49K, 0,760,200,580,785,74.65K, 40,1200,540,760,980,74.3K, 40,1760,540,700,955,73.75K, 80,1960,780,840,965,73.37K, 160,1880,820,960,960,72.86K, 520,2120,1080,1000,950,73.73K, 520,2320,1340,880,1005,72.76K, 680,2480,2060,1020,1075,71.49K, 1080,2760,2040,1000,1050,70.52K, 0,0,0,0,75,77.22K, 0,0,0,0,165,77.85K, 0,0,20,100,215,77.23K, 0,80,120,240,340,76.46K, 0,320,280,280,445,74.91K, 80,280,200,320,425,75.43K, 40,200,440,400,460,75K, 80,320,340,440,505,75.68K, 280,320,420,400,595,75.47K, 320,520,560,340,520,75.52K, 520,440,660,500,530,74.28K, 600,480,640,560,500,74.17K, 0,0,0,0,0,84.57K, 0,0,0,340,2865,83.23K, 0,800,20,1660,5480,80.32K, 0,3120,80,4740,7555,76.42K, 80,17.8K,1560,8660,10.05K,63.85K, 720,32.2K,4820,10.42K,11.12K,50.6K, 680,37.92K,5420,10.14K,11.4K,46.85K, 1200,40.24K,6720,12.16K,11.68K,42.03K, 1320,46.2K,8960,11.9K,11.4K,37.48K, 3360,53.68K,11.06K,11.58K,11.53K,29.63K, 4960,60.52K,13.5K,12.84K,12.11K,21.34K, 7680,70.64K,19.18K,12.8K,12.66K,8625, 11.04K,74.84K,21.64K,14.42K,12.79K,0, 0,0,0,260,2185,82.97K, 0,600,20,1600,3685,81.96K, 0,3040,240,3380,5715,78.06K, 40,14.04K,1400,6360,7555,68.48K, 720,24.92K,3620,7940,8545,58.89K, 880,29.08K,4400,8200,8155,54.96K, 1600,31.96K,5920,8160,8625,52.67K, 1920,34.56K,6440,8640,8585,48.87K, 2960,39.84K,8780,9100,9070,42.05K, 3800,44.92K,11.34K,9520,8970,36.24K, 6360,52.72K,14.52K,9460,9035,26.23K, 8400,55.88K,16.9K,9780,9090,19.94K, 0,40,0,240,1855,83.54K, 0,680,0,1220,3615,81.78K, 0,2320,180,3240,4970,79.77K, 80,12.28K,1020,5720,7075,70.7K, 480,21.88K,3800,7460,7330,60.98K, 840,24.92K,4800,7880,7515,59.3K, 1120,27.12K,5880,8000,7705,54.69K, 1560,29.6K,5760,8200,7690,52.4K, 3000,34K,8180,8460,8000,46.95K, 3800,38.96K,11.02K,8360,8085,41.02K, 6560,44.76K,14.16K,8800,8330,32.27K, 8120,48.76K,15.5K,8960,8540,26.28K, 0,0,0,160,1280,85.68K, 0,360,20,1020,2700,83.46K, 0,1600,120,2180,3350,80.73K, 120,9240,860,4120,4370,74.96K, 400,14.84K,2660,5160,5000,68.52K, 680,17.12K,3300,5140,4870,66.78K, 720,19.44K,3720,5500,4905,63.91K, 1320,21.44K,4780,5580,5020,61.8K, 2240,24.16K,6280,5780,5290,58.92K, 2600,27.6K,8040,5980,5180,54.88K, 4440,29.92K,10.4K,6020,5500,49.06K, 6360,32.88K,11.94K,6300,5380,43.38K, 0,0,0,40,450,84.1K, 0,120,0,160,685,84.61K, 0,280,0,620,1060,83.1K, 40,1920,440,1060,1370,82.03K, 120,3320,880,1540,1595,80.28K, 320,3840,1220,1460,1730,79.7K, 240,4520,1480,1600,1515,79.27K, 480,4720,1920,1540,1680,78.15K, 480,5160,2220,1520,1815,76.88K, 1120,5440,2920,1680,1860,75.97K, 2160,6040,3220,1920,1820,74.48K, 2280,6160,3900,2020,1765,73.13K, 0,0,0,20,250,85.16K, 0,40,0,100,435,83.35K, 0,120,20,460,565,84.07K, 0,920,300,500,905,84K, 40,1280,660,860,1075,83.07K, 160,1720,740,660,1020,81.74K, 320,1920,880,760,1155,81.42K, 200,2120,1180,840,1045,81.32K, 400,2320,1200,980,1090,80.8K, 760,2320,1780,980,1070,81.11K, 1200,2840,1800,900,980,78.4K, 1560,2840,2300,960,1215,79.25K, 0,0,0,0,80,85.09K, 0,0,0,20,175,84.75K, 0,0,0,100,210,86.27K, 0,80,140,180,395,84.48K, 40,240,280,320,535,82.94K, 80,320,420,280,565,82.64K, 120,360,440,240,580,83.47K, 280,360,440,440,485,84.08K, 240,480,540,460,575,84.06K, 360,440,640,460,490,83.36K, 560,520,760,480,555,82.23K, 760,600,720,420,515,82.63K, 0,0,0,0,0,97.33K, 0,40,0,340,3020,95.22K, 0,1000,0,2460,5710,92.76K, 0,4600,180,4820,7890,87.83K, 200,21.24K,2120,8920,10K,72.82K, 1160,37.84K,6180,10.26K,11.09K,58.66K, 1760,42.64K,7120,11.24K,11.43K,54.04K, 2360,46.76K,8780,11.5K,11.92K,49.49K, 2920,50.92K,10.74K,11.82K,12.03K,43.89K, 4360,58.64K,14.48K,12.84K,12.24K,34.16K, 7520,67K,16.68K,13.08K,12.28K,24.95K, 12.4K,76.76K,21.3K,13.38K,13.02K,9890, 14.56K,82.76K,24.32K,14.24K,12.75K,0, 0,40,0,320,2320,95.83K, 0,520,20,1840,4155,91.8K, 0,4440,180,3140,5830,90.2K, 240,16.32K,1740,6640,7850,78.47K, 1160,28.24K,4820,8280,8355,67.44K, 1160,32.12K,6200,8760,8410,63.88K, 2040,35.04K,6880,8800,8425,59.83K, 2320,38.2K,8800,9220,8360,57.1K, 3960,44.6K,11.5K,9300,8920,49.29K, 6040,49.72K,13.44K,10.08K,8780,41.56K, 8760,56.96K,18.12K,9820,9185,30.67K, 12.6K,61K,20.58K,10.02K,9265,22.94K, 0,0,0,240,2055,95.38K, 0,600,0,1720,3860,95.58K, 0,3080,160,3720,5190,91.14K, 200,13.6K,1980,6480,6820,81.25K, 960,25.24K,4700,7040,7535,70.85K, 1040,28.64K,5460,7780,7665,66.49K, 1880,30.64K,7400,7440,7975,62.8K, 2960,32.88K,8440,8040,8150,60.46K, 3040,39.08K,11K,8500,8210,54.12K, 4760,42.6K,13K,8600,8315,46.06K, 10.52K,48.32K,16.3K,8920,8240,36.21K, 12K,51.16K,18.88K,9160,8560,30.32K, 0,0,0,240,1420,96.67K, 0,600,0,1000,2915,94.86K, 0,2120,100,2140,3820,93.1K, 280,10.4K,1200,4460,4470,86.13K, 880,17.56K,3200,4920,5055,78.24K, 1080,19.76K,4260,4840,4995,76.78K, 1600,21.6K,5040,5460,4950,73.42K, 1680,23.4K,5960,5620,5240,71.87K, 2760,26.72K,8020,5900,5355,68.24K, 4160,29.84K,9680,5800,5470,61.76K, 6240,33.44K,12.4K,5800,5305,54.53K, 8560,35.2K,14.04K,6380,5460,50.61K, 0,0,0,80,445,99K, 0,80,0,260,770,96.4K, 0,440,60,660,1120,96.14K, 0,2440,360,1240,1345,94.21K, 320,3720,1020,1340,1600,91.9K, 440,4160,1560,1580,1725,92.01K, 440,4960,1920,1440,1690,91.6K, 760,5080,2320,1460,1630,89.85K, 1280,5520,2660,1660,1660,88.83K, 1520,5800,3440,1620,1950,86.51K, 2560,6800,3980,1620,1615,85.96K, 3440,6560,4700,1820,1780,84.25K, 0,0,0,20,215,97.68K, 0,0,0,120,515,96.45K, 0,280,0,300,605,96.55K, 0,1160,200,560,1010,94.96K, 160,1680,680,800,945,94.28K, 200,1800,780,920,985,93.31K, 520,2040,1180,920,1015,93.55K, 440,2480,1340,860,1025,94.01K, 600,2800,1380,780,1145,91.61K, 1000,2680,1920,940,1045,91.1K, 1760,3160,2020,860,1155,89.96K, 2000,3040,2300,1140,1035,90K, 0,0,0,0,85,97.28K, 0,0,0,20,245,96.72K, 0,40,20,60,260,97.53K, 0,40,140,300,375,98.53K, 80,360,300,300,455,97.26K, 120,320,320,340,495,96.32K, 200,360,500,380,540,97.54K, 280,360,520,360,555,96.18K, 240,400,740,420,605,94.9K, 720,560,480,360,510,96.65K, 880,480,860,460,580,95.13K, 920,480,760,620,525,93.8K, 0,0,0,0,0,112.2K, 0,160,0,440,3510,110.5K, 0,1280,0,2780,5945,106.9K, 0,6600,360,5380,8230,101.8K, 280,24.12K,3200,9640,10.58K,83.76K, 1680,43.24K,7660,11.84K,11.44K,67.41K, 2800,47.76K,9840,11.9K,11.83K,61.3K, 3880,52.2K,11.18K,12.6K,12.26K,55.81K, 4280,55.96K,13.48K,12.88K,12.07K,49.71K, 7040,66K,17.74K,12.7K,12.52K,39.19K, 10.12K,73.6K,21.76K,13.68K,12.42K,28.1K, 15.6K,84.48K,26.16K,13.86K,12.74K,10.91K, 19.6K,88.4K,30.34K,15.44K,13.01K,0, 0,80,0,320,2655,109.8K, 0,1120,40,1780,4800,108.5K, 0,4520,340,4000,6410,104K, 240,19.12K,2280,7080,8175,91.44K, 1400,31.8K,6400,8640,9125,77.22K, 1920,35.84K,7900,8800,8620,73.78K, 2880,39.48K,9520,9360,8695,68.99K, 3640,42.76K,10.9K,9740,9000,65.21K, 5760,49.6K,14.06K,9620,9025,56.09K, 8440,54.48K,16.78K,10.14K,9470,47.81K, 13.36K,61.64K,20.58K,10.72K,9065,34.76K, 17.36K,67K,23.6K,10.4K,9225,25.95K, 0,80,0,360,2250,111.6K, 0,920,40,1720,4495,107.5K, 0,4120,240,3700,5670,104.6K, 320,16.76K,2100,6540,7090,92.79K, 1200,27.8K,5860,8040,7825,81K, 2360,31.96K,7220,7560,8160,76.21K, 2640,34.56K,8960,8300,8140,73.86K, 3600,37.36K,9760,7960,8055,68.69K, 5480,42.12K,13.34K,8780,8740,61.73K, 8640,46.12K,15.92K,8940,8435,54.66K, 13.16K,52.84K,20.48K,9320,8255,42.1K, 17K,55.96K,22.6K,9260,8555,34.46K, 0,80,20,220,1545,110.7K, 0,840,0,1460,2980,109K, 40,2960,120,2720,3850,106.3K, 200,11.52K,1880,4800,4645,97.89K, 960,20.24K,4420,5380,4840,89.38K, 1400,22.12K,5600,5600,5150,87.06K, 2000,23.92K,6940,5420,5145,85.41K, 2960,27.16K,7540,5360,5305,81.73K, 3960,29.16K,9740,5760,5550,76.66K, 6600,31.48K,11.98K,6180,5485,72K, 9280,36.36K,14.14K,6260,5515,63.64K, 12.88K,37.48K,16.38K,6360,5565,58.44K, 0,0,0,120,365,111.3K, 0,120,0,400,785,112K, 0,600,60,720,1105,111K, 0,2680,840,1340,1620,107.6K, 360,4200,1480,1540,1665,105.9K, 560,4640,1600,1660,1670,105.7K, 640,5240,2140,1580,1705,102.6K, 960,5160,2520,1700,1750,103.5K, 1440,5760,3440,1840,1705,102.5K, 2120,6040,3600,1940,1715,100.7K, 3600,6400,4960,1920,1865,98.63K, 4480,6920,4780,1800,1845,97.68K, 0,0,0,20,270,111.8K, 0,0,0,220,445,112.7K, 0,240,20,420,660,111.7K, 120,1440,360,640,935,110K, 280,1920,1040,820,1085,109.1K, 320,2160,1160,1040,1020,108.6K, 440,2680,1060,880,1180,108.6K, 520,2200,1660,920,1245,106.7K, 1080,2680,1740,1000,1055,105.8K, 1680,2880,1900,980,1220,104.7K, 2080,3360,2560,1040,1080,104.2K, 3120,3120,2680,1060,1070,104.1K, 0,0,0,0,65,111.8K, 0,0,0,40,210,112K, 0,40,0,160,310,113K, 0,200,180,420,385,111.7K, 240,320,320,300,500,111.9K, 200,400,360,360,515,112K, 240,360,620,480,520,109.8K, 280,480,600,400,490,111.3K, 320,480,740,480,510,110K, 760,560,840,420,465,109.1K, 840,560,1020,460,510,109.5K, 1240,720,940,440,580,107.9K, 0,0,0,0,0,126.5K, 0,240,20,660,3510,125.1K, 0,1560,40,3240,6490,122K, 0,8360,560,5580,8525,115K, 480,29K,4060,9780,10.92K,95.24K, 2440,49K,9580,11.62K,11.9K,77.16K, 3640,53.4K,12.1K,12.56K,12.09K,69.99K, 4440,59.04K,14.62K,12.56K,12.26K,62.64K, 6200,64.28K,15.58K,12.5K,12.59K,57.18K, 9640,72.48K,20.12K,14.16K,12.38K,45K, 13K,82.2K,26.08K,13.54K,12.68K,32.44K, 21.56K,91.56K,30.4K,14.34K,12.96K,12.91K, 27.04K,99.64K,35.02K,14.04K,12.93K,0, 0,120,0,720,2800,125.9K, 0,1440,80,2300,4855,122K, 0,6000,260,4820,6345,118.9K, 560,22.4K,3460,7800,7710,103.5K, 1720,36.6K,8700,8420,8635,88.01K, 3200,40.2K,9680,8860,8975,83.21K, 3800,44.2K,11.4K,9400,9210,78.15K, 5720,47.16K,13.5K,9700,9275,73.54K, 8800,55.08K,16.22K,9980,9155,63.38K, 12.04K,60.32K,20.14K,9920,9240,54.21K, 18.12K,66.52K,24.76K,10.56K,9755,38.51K, 23.12K,70.6K,27.5K,10.86K,9445,29.79K, 0,160,0,460,2545,126K, 0,1320,60,2540,4380,123.1K, 0,5400,280,3680,5795,119.2K, 320,19.24K,3640,6920,7730,104.9K, 2080,31.36K,7440,8580,7910,91.25K, 3200,35.28K,9580,8300,7780,88.89K, 4160,38.48K,10.36K,8480,8320,82.36K, 5720,41.84K,12.08K,8460,8310,79.04K, 7960,45.92K,15.14K,9280,8670,70.13K, 11.64K,50.08K,18.88K,9900,8665,61.79K, 18.12K,56.56K,23.46K,9120,8790,48.56K, 21.12K,60.32K,26.22K,9880,8460,39.26K, 0,120,0,420,1570,125.4K, 0,1120,20,1880,3015,125.1K, 0,3400,320,3140,4115,120.4K, 160,13.16K,2280,5140,4820,112.1K, 1560,23.28K,5360,5140,5270,103.6K, 2120,24.52K,6740,5440,5285,98.62K, 3000,26.72K,7860,5620,5165,95.69K, 3840,29.52K,8620,5560,5135,94.6K, 6120,31.32K,11.98K,6380,5450,88.23K, 8240,35.04K,12.84K,5880,5455,81.25K, 12.28K,38.36K,17.66K,6660,5510,71.88K, 15.96K,39.88K,18.34K,5960,5520,65.63K, 0,0,0,100,480,128.3K, 0,200,40,380,820,128.1K, 0,1160,140,820,1085,124.4K, 120,3120,640,1200,1535,123K, 520,4600,2060,1640,1675,122.6K, 720,5080,2560,1720,1765,120.4K, 840,5840,2660,1600,1720,117.9K, 1400,5800,3020,1720,1700,117.9K, 2200,5960,3920,1760,1815,115K, 2960,6520,4200,1740,1870,115.1K, 4920,6920,4700,1860,1815,110.9K, 5880,7200,5340,1820,1850,110.7K, 0,0,0,40,280,126.4K, 0,40,20,260,395,126.2K, 0,440,20,400,755,125.5K, 120,1560,340,680,925,125K, 360,2000,1040,980,1120,123.3K, 520,2440,1620,1060,1060,122.5K, 600,2720,1420,1100,1050,123.5K, 1400,2400,1500,1040,1190,122.5K, 1840,2840,1840,1100,1045,122K, 2240,3080,2180,1060,1245,119.7K, 2760,3440,2720,1160,1135,119.2K, 3640,3280,3020,1020,1155,117.1K, 0,0,0,0,70,126.9K, 0,0,20,20,270,127.1K, 0,80,0,140,370,126.7K, 40,240,200,320,480,126.9K, 200,400,480,360,445,126.1K, 240,440,620,340,585,124.4K, 600,480,500,440,515,126.1K, 480,560,580,480,500,126.1K, 680,640,740,420,510,125.7K, 1200,440,780,560,525,123.7K, 1520,440,860,540,615,124.8K, 1680,480,940,560,695,122.6K, 0,0,0,0,0,141K, 0,160,0,1040,4040,138.9K, 40,2120,40,3640,6805,133.6K, 40,9680,680,6720,9025,126.5K, 840,33.4K,4540,10.52K,11.04K,105.1K, 3440,54K,11.58K,12.22K,12.02K,84.44K, 5080,58.44K,14.02K,13K,12.36K,77.37K, 6520,64.16K,16.1K,13.58K,12.38K,71.43K, 8320,69.92K,18.98K,13.42K,12.61K,64.57K, 12.24K,79.4K,23.42K,13.82K,12.83K,49.19K, 18.28K,88.32K,27.62K,14.36K,12.92K,35.92K, 26.08K,98.32K,34.7K,14.64K,12.89K,13.91K, 32.52K,105.5K,37.96K,15.1K,12.97K,0, 0,240,0,700,3085,139.9K, 0,1600,20,2640,5350,136.3K, 40,7400,440,4980,6965,129.8K, 760,24.84K,4200,8200,8465,114K, 3000,39.6K,9460,9520,8885,99.29K, 4080,44.84K,11.36K,8660,9110,92.24K, 5720,49.08K,13.08K,9900,8710,87.12K, 6680,51.92K,15.44K,9800,9060,81.99K, 10.48K,59.44K,18.98K,9280,9425,71.08K, 15.08K,64.56K,21.74K,10.24K,9075,59.72K, 22.36K,71.76K,27.44K,11.14K,9225,43.48K, 28.2K,76.04K,30.62K,10.58K,9335,33.11K, 0,40,0,820,2795,139.1K, 0,1760,60,2620,4655,136.4K, 40,6080,620,4680,5895,130.4K, 680,22.16K,3860,7160,7530,117.7K, 2800,34.68K,9080,8660,8120,102.2K, 3560,39.76K,10.58K,8020,8225,97.99K, 5040,42.44K,12.82K,8000,8345,91.41K, 6520,45.16K,13.9K,8720,8550,88.36K, 10.32K,50.32K,17.8K,8840,8420,77.92K, 15.08K,54.36K,21.04K,9400,8360,67.46K, 20.32K,59.08K,25.28K,10.06K,8505,53.46K, 26.52K,63.24K,28.4K,9880,8595,43.33K, 0,160,20,400,1925,140.6K, 0,1360,20,1760,3495,137K, 40,4440,340,2920,4260,134.6K, 440,16.16K,2840,5060,4675,123.4K, 2000,24.84K,6420,5620,5455,114.2K, 3040,27.2K,7660,5780,5155,109.6K, 3920,29.72K,9080,5400,5140,106.6K, 5240,30.84K,10.8K,5900,5360,103.4K, 6680,33.24K,14.08K,6540,5385,97.67K, 11.32K,36.76K,15.42K,6340,5565,89.7K, 15.64K,39.6K,18.78K,6920,5625,79.22K, 20.4K,41.8K,20.84K,6360,5500,73.75K, 0,0,0,100,565,139.2K, 0,240,20,420,1000,139.3K, 0,1200,80,1000,1330,139.6K, 120,3400,1240,1460,1695,135.9K, 680,4680,2380,1780,1700,133.8K, 880,5760,2280,1560,1860,132.4K, 1440,6040,3140,1840,1750,131.9K, 1720,6320,3280,1760,1705,129K, 2720,6200,3940,2020,1790,130.4K, 3680,6520,4880,1800,1960,127.1K, 5440,7880,5640,1800,1850,121.7K, 7240,7320,5900,2060,1805,121.9K, 0,0,0,40,290,140.4K, 0,120,20,320,505,140.2K, 0,480,60,500,795,138.4K, 120,1760,640,760,995,138.5K, 720,2400,1060,880,1175,136.9K, 600,2720,1620,940,1045,134.8K, 880,2760,1920,1000,1135,136.3K, 1160,2760,2160,980,1225,134.4K, 1800,3280,2420,1000,1065,134.4K, 2520,3040,2880,1240,1090,132.1K, 3680,3360,3020,1100,1265,132.4K, 4480,3520,2900,1080,1185,132K, 0,0,0,0,95,141.4K, 0,40,0,60,245,140.9K, 0,40,20,100,375,138.8K, 80,200,200,360,525,140.2K, 160,480,500,280,625,138.7K, 360,440,680,400,540,139.3K, 600,480,740,400,520,137K, 440,520,680,500,490,138.1K, 840,520,900,440,590,138.5K, 1080,640,1020,480,490,135.5K, 1680,640,1160,500,570,137K, 1800,560,1260,540,655,137.3K, 0,0,0,0,0,151.7K, 0,280,0,1160,4415,146.8K, 0,3080,140,3960,7025,142.4K, 0,10.96K,680,6940,9320,135.9K, 1160,36.68K,5600,11.66K,11.24K,112.2K, 4200,58.28K,13.22K,12.12K,12.45K,90.54K, 5320,64.64K,15.06K,13.06K,12.38K,83K, 7280,70.92K,17.96K,13.42K,12.09K,75.58K, 9720,75.2K,20.02K,14.16K,12.73K,68.18K, 15.08K,83.48K,25.64K,14.98K,12.65K,51.7K, 19.56K,92.24K,31.38K,14.36K,13.19K,37.35K, 30.6K,104.2K,36.58K,14.78K,13.04K,15.16K, 38.72K,111.7K,39.72K,14.86K,13.09K,0, 0,160,0,780,3420,147.3K, 0,2200,40,3140,5400,144K, 0,8160,440,5860,6795,137.7K, 840,28.36K,4780,8100,8140,121.6K, 3400,43.16K,10.22K,9240,8640,103.9K, 5560,47.84K,11.72K,9260,9135,99.2K, 5720,52.08K,14.48K,10.02K,9355,93.34K, 8320,55.32K,16.88K,9700,9335,87.67K, 12.64K,62.84K,20.44K,9800,9305,74.16K, 16.56K,68.08K,23.54K,10.4K,9100,63.85K, 25.76K,75.68K,29.44K,10.62K,9905,46.67K, 32.56K,79.32K,31.9K,10.24K,9935,33.6K, 0,240,0,800,2980,148.5K, 0,2320,80,2920,4890,145.6K, 40,7520,520,4320,6425,140.4K, 960,23.4K,4000,8160,7655,123.2K, 3280,37.6K,9880,8120,8085,108.5K, 4880,41.76K,11.54K,8860,8330,102.2K, 6000,44.48K,13.72K,8780,8420,100.3K, 8200,47.44K,15.12K,8700,8695,93.43K, 11.4K,52.32K,19.68K,9080,8855,83.11K, 16.64K,57.64K,22.58K,9400,8730,72.99K, 25.2K,62.36K,28.64K,9700,8880,56.19K, 29.88K,65.08K,31.64K,10.78K,8855,45.84K, 0,200,0,480,2015,148.3K, 0,1640,100,1740,3460,147.3K, 0,4840,480,3600,4275,143.1K, 600,18K,3260,4820,4995,132.7K, 2440,26.2K,7320,5500,5255,120K, 3720,28.96K,8100,5920,5250,119K, 4480,30.92K,10.28K,5700,5130,114.7K, 5920,32.8K,10.98K,6200,5190,111.4K, 8240,34.64K,14.38K,6600,5380,104K, 11.72K,38.56K,15.7K,6880,5405,95.9K, 18.64K,40.64K,19.56K,6520,5655,84.75K, 22.96K,42.32K,21.86K,6860,5440,78.42K, 0,40,0,180,490,152.1K, 0,280,40,520,950,149K, 0,1160,40,1000,1340,148K, 240,3840,1220,1400,1760,146.3K, 1000,5440,2360,1600,1695,141.6K, 1240,6160,3140,1440,1920,140.8K, 1480,6040,3320,1800,1705,142.6K, 2280,6440,3440,1740,1700,140.8K, 3320,6880,4480,2000,1690,137.3K, 4320,6800,4920,1960,1820,135.1K, 7080,7600,5440,2000,1930,132.4K, 8440,7680,5880,2100,1900,130K, 0,0,0,40,350,150.5K, 0,200,0,260,565,149.4K, 0,440,80,500,805,150.3K, 120,1840,640,920,1085,148.4K, 600,2280,1440,980,1200,147.6K, 880,2600,1940,960,1140,146.5K, 1080,2520,1840,1160,1265,145.3K, 1200,3120,2100,940,1155,143.9K, 1880,3360,2660,840,1165,143.4K, 2680,3320,2980,1080,1255,140.8K, 4000,3520,2840,1180,1130,141.1K, 4960,3600,3320,1080,1210,138.4K, 0,0,0,0,100,149.6K, 0,0,0,120,255,150.5K, 0,80,40,140,320,151.3K, 120,320,300,340,445,149.7K, 480,480,480,460,610,148.8K, 360,440,520,440,590,150.6K, 680,400,600,480,595,149.7K, 720,440,740,500,545,148.2K, 920,440,1080,400,590,146.8K, 1160,520,1040,560,580,147.8K, 1600,560,1080,600,590,146.7K, 2200,720,880,480,540,145.2K, 0,0,0,0,0,155K, 0,240,0,1220,4495,151.4K, 0,3200,80,4360,7275,147.4K, 0,12K,620,7280,9355,139.5K, 1200,39.24K,5560,11.58K,11.5K,117.2K, 4320,61.92K,13.5K,12.4K,12.14K,92.23K, 6160,66.52K,16.24K,13.04K,12.78K,84.64K, 8320,72.6K,18.74K,14.34K,12.41K,76.7K, 10.48K,77.96K,21.12K,13.98K,13.03K,70.32K, 15.84K,87K,27.02K,13.54K,12.95K,54.91K, 21.24K,95.76K,31.08K,13.8K,13.09K,38.73K, 33.4K,107.6K,36.82K,14.7K,13.04K,15.25K, 41.8K,113.7K,41.68K,15.26K,13.63K,0, 0,280,0,920,3315,153.4K, 0,2880,80,3240,5450,148.7K, 40,9200,700,5720,6855,144.9K, 1040,29.92K,4920,8300,8215,125.2K, 3520,45.6K,11.68K,9440,8765,106.1K, 5200,49.44K,13.3K,9720,8830,101.5K, 6720,54.84K,15.66K,9860,8845,96.12K, 8560,57.32K,16.98K,10.02K,9165,89.74K, 13.72K,64.36K,21K,10.14K,9035,77.83K, 18.2K,70.28K,25.82K,10.8K,9615,66.02K, 27.56K,77.32K,30.74K,10.14K,9785,48.24K, 33.56K,80.56K,33.74K,10.88K,9690,36.6K, 0,320,0,920,2940,151.1K, 0,2160,100,3100,4925,151.7K, 0,8400,800,4660,6385,144.2K, 720,26.12K,4700,7660,7710,128.6K, 4080,38.84K,10.7K,8780,7860,112.8K, 5000,42.32K,12.38K,9320,8365,106.9K, 7120,45.76K,14.56K,9020,8450,101K, 8480,48.64K,16.32K,8960,8360,96.27K, 13.04K,53.64K,20.34K,9660,8410,87.02K, 17.12K,58.84K,22.64K,9580,8820,73.71K, 26.76K,62.88K,27.38K,9880,8905,58.73K, 32.56K,68K,31.2K,9800,8820,47.83K, 0,280,0,640,2025,151.9K, 0,1640,20,1720,3675,152.7K, 40,5760,380,3720,4110,148.3K, 680,19.32K,3200,5280,4895,136.3K, 3040,27.28K,7640,5600,5130,124.2K, 3840,29.64K,9360,5740,5455,120.7K, 4920,31.4K,11.04K,5700,5185,116.8K, 6680,34.56K,11.96K,5840,5435,113.7K, 9680,36.52K,14.36K,6120,5240,106.2K, 13.44K,39.44K,17.52K,5940,5545,98.73K, 20.24K,42.8K,20.04K,6120,5545,89.53K, 22.92K,43.44K,22.06K,6560,5590,80.87K, 0,0,0,180,645,155.6K, 0,280,0,660,1005,153.1K, 0,1280,160,1080,1290,151.6K, 120,3880,1420,1580,1730,149.8K, 1120,5560,2840,1480,1805,146.1K, 1400,6400,3040,1480,1705,145K, 1760,6040,3680,1820,1745,145.2K, 2440,6520,3860,1880,1575,142.6K, 3440,6880,4900,2000,1580,141.3K, 5280,7160,5180,1900,1860,138.8K, 6800,7400,5820,1980,1920,136.6K, 9160,7920,5900,2180,1770,133.7K, 0,0,0,40,370,154.1K, 0,280,0,240,580,156.1K, 0,440,160,500,855,155K, 80,2080,780,960,930,152.5K, 720,2560,1600,920,1105,149.9K, 880,2800,1700,980,1125,149.6K, 1040,2720,1980,1120,1115,148.8K, 1880,3040,2000,1000,1260,148.7K, 2040,3240,2560,1040,1215,148.1K, 3400,3440,2320,1120,1230,148.1K, 4600,3560,2880,1180,1220,144.5K, 5240,3800,3360,1020,1175,144.4K, 0,0,0,20,165,156K, 0,0,40,140,265,155.9K, 0,160,40,100,355,153.4K, 120,320,300,340,455,154.1K, 360,560,560,400,530,153.9K, 400,600,560,520,500,152.1K, 720,520,620,380,565,152.5K, 640,680,820,420,555,152.3K, 1200,480,860,540,615,152.5K, 1240,640,860,460,505,150.7K, 1880,640,1060,640,525,150.4K, 2600,560,1160,640,590,150.5K, 0,0,0,0,0,153.7K, 0,320,0,1140,4510,151.5K, 0,3040,100,4180,7595,146.8K, 0,12.68K,860,7280,9205,139.6K, 840,40K,5640,11.16K,11.51K,116.2K, 4800,62.96K,13.38K,12.68K,12.39K,92.57K, 6360,67.64K,15.6K,14K,12.7K,85.62K, 8080,74.64K,18.6K,13.36K,12.88K,78.13K, 10.16K,78.36K,21.82K,13.66K,12.47K,70.85K, 16.76K,88.04K,26.4K,14.24K,13.28K,54.48K, 21.8K,97.36K,30.78K,14.24K,13.44K,39.06K, 32.6K,108.5K,38.46K,14.88K,13.08K,14.8K, 39.44K,115K,42.46K,15.36K,13.5K,0, 0,280,0,880,3215,153.8K, 0,2520,100,3480,5460,149.1K, 40,9480,480,5180,7440,141.5K, 800,30.6K,5300,8200,8515,124.4K, 3640,46.16K,10.56K,9320,8855,106.3K, 4960,50.52K,13.42K,9460,9090,101K, 6600,55.4K,15.2K,9500,9115,95.46K, 9520,58.52K,16.76K,9980,9295,88.44K, 14.08K,63.76K,20.72K,10.2K,9345,77.54K, 18.88K,70.32K,24.42K,10.64K,9805,64.67K, 27.56K,77.76K,30.56K,10.52K,9425,47.9K, 35.24K,81.08K,33.02K,10.86K,9885,36.1K, 0,240,0,860,3190,154.3K, 0,2440,20,3040,4760,150.5K, 40,8240,540,4900,6390,143.9K, 1240,26.52K,4520,7520,8050,128.7K, 3920,39.28K,10.24K,8740,8335,111.8K, 4800,44K,12.58K,8740,8260,107.6K, 6560,46.16K,14.58K,9220,8690,102.7K, 8760,49.92K,15.8K,9260,8470,95.61K, 12.92K,53.92K,20.36K,9900,8695,86.53K, 17.48K,59.96K,24.84K,9600,8260,74.4K, 26.28K,65K,28.08K,9560,8705,58.93K, 31.4K,67.84K,31.26K,10.4K,8845,47.41K, 0,200,0,660,2240,153.3K, 0,1800,120,2100,3390,149.5K, 40,6040,460,3560,4160,146.2K, 720,19.32K,3440,5160,4870,138.3K, 2560,27.48K,8180,5460,5340,123.6K, 3840,29.32K,9520,6040,5530,121.3K, 4600,32.28K,10.4K,5800,5370,117.7K, 6080,33.68K,11.96K,5880,5875,112.5K, 9120,37.28K,14.4K,6200,5420,106K, 12.4K,39.68K,17.36K,6120,5705,100.1K, 19.56K,42.8K,21.56K,6400,5410,87.71K, 23.6K,42.68K,23.44K,6620,5670,80.6K, 0,40,0,120,615,154K, 0,440,0,700,890,153.7K, 0,1320,220,800,1330,152.3K, 360,3840,1320,1500,1715,150K, 880,4920,2900,1920,1840,146K, 1320,5880,2980,1920,1705,146.2K, 2000,6200,3280,2100,1495,144.9K, 2200,6440,3720,1960,1775,142.2K, 4000,6840,4520,1880,1645,142.9K, 4840,7440,4860,1680,1805,138.4K, 6560,7880,5560,2040,1910,138.4K, 8680,8040,6140,1880,1855,136.4K, 0,0,0,180,295,155K, 0,160,20,300,615,155K, 0,720,100,460,760,153.5K, 280,1800,580,820,1100,152.4K, 560,2480,1560,1020,1125,148.9K, 1200,2960,1840,1000,1080,149K, 1040,2600,2080,1260,1055,149K, 1400,2800,2240,1080,1210,147.5K, 2240,3120,2420,1120,1225,146K, 3040,3480,2540,1280,1135,145.4K, 3920,3360,2880,1280,1175,144.4K, 5160,3640,3220,1320,1125,143.1K, 0,0,0,0,125,154.6K, 0,0,40,80,265,155.6K, 0,80,0,200,380,154K, 120,320,380,280,505,153.6K, 200,520,700,360,580,153.1K, 320,520,740,460,590,151.8K, 560,560,700,420,535,153.8K, 960,560,620,500,440,152.5K, 1040,600,900,500,655,150.6K, 1520,800,1000,420,590,149.3K, 1640,680,1120,560,665,151.7K, 2360,520,1260,640,565,150.2K, 0,0,0,0,0,150.3K, 0,440,0,1260,4535,148.4K, 0,3440,20,3880,7240,143.2K, 40,11.36K,620,7920,9055,136.8K, 1160,39.04K,5580,11.42K,11.42K,113K, 4240,62.16K,12.32K,13.24K,12.19K,91.24K, 5560,68.48K,15.74K,12.94K,12.65K,85.09K, 7400,72.56K,18.16K,13.74K,12.32K,75.5K, 10K,77.88K,21.18K,13.92K,12.99K,68.1K, 14.68K,87.08K,26.38K,14.72K,12.5K,51.51K, 19.76K,97.64K,29.6K,14.78K,12.6K,37.59K, 31.6K,107.4K,37.14K,14.92K,13.04K,15.39K, 38.72K,114.9K,41.62K,15.18K,12.77K,0, 0,160,0,800,3350,148.5K, 0,2400,20,3320,5615,145.7K, 0,9640,620,5660,7080,139.9K, 1080,29.08K,4720,8820,8600,122.7K, 3280,45.72K,11.04K,9380,9375,104.9K, 4920,49K,12.44K,9840,8915,98.35K, 6280,53.44K,13.92K,9820,9495,94.49K, 9200,57.48K,16.62K,10.52K,9280,87.06K, 12.96K,63.76K,20.48K,10.5K,9410,76.12K, 17.92K,70K,23.94K,10.7K,9540,63.25K, 25.84K,77K,28.9K,10.5K,9560,46.8K, 30.88K,81.4K,33.46K,11.04K,9100,34.48K, 0,200,0,740,3030,149.5K, 40,2320,120,2920,4795,146.1K, 40,8200,340,5020,6235,139.9K, 560,26.48K,4260,7180,7815,126K, 3480,40.48K,9400,8280,8355,109.4K, 4720,43.8K,11.64K,8480,8335,103K, 5760,46.48K,14.06K,8980,8365,98.44K, 8120,50.24K,15.2K,9340,8580,94.08K, 12.88K,54.4K,18.84K,9440,8705,83.16K, 15.8K,59.2K,22.38K,9360,8515,71.78K, 23.24K,63.8K,27.9K,10.64K,8425,56.67K, 31.12K,67.28K,30.34K,9760,8985,46.45K, 0,320,0,640,2220,151K, 0,1640,60,2040,3350,147.8K, 40,5480,440,3620,4300,142.2K, 640,18.84K,3360,4880,5190,133.5K, 2800,26.96K,6980,5780,5065,121.9K, 3720,29.8K,8540,5740,5210,116.8K, 4640,31.76K,9720,5840,5550,114.1K, 6040,33.36K,11.78K,5840,5395,110.2K, 8720,37.36K,14.16K,6020,5425,105K, 11.64K,39.96K,16.92K,6020,5420,96.81K, 19K,41.56K,19.52K,6640,5650,85.6K, 22.48K,42.96K,21.88K,6620,5425,78.21K, 0,40,0,140,575,150.3K, 0,280,0,540,1025,150.5K, 40,1320,100,820,1345,147.4K, 120,3840,1420,1560,1620,145.6K, 1320,5480,2420,1640,1765,141.9K, 1040,6040,2720,2040,1800,142.6K, 1760,5960,3820,1660,1925,141.5K, 1840,7040,3940,1460,1735,140.3K, 3360,6440,4140,2100,1780,138K, 4760,7240,4740,1920,1775,136.6K, 6520,7680,5980,2000,1920,132.6K, 8040,7720,6000,1980,1705,131K, 0,0,0,100,380,151.3K, 0,80,0,360,550,150.5K, 0,560,120,520,755,150.1K, 240,1720,560,600,1055,149K, 560,2640,1540,960,1110,147.1K, 760,2600,1760,1180,1065,145.3K, 960,2720,2060,1080,1015,144.8K, 1480,2960,2160,1180,1015,144K, 1880,3240,2600,1020,1100,143.7K, 2680,3440,2620,1320,1080,140.9K, 3960,3600,3020,1220,1150,139.8K, 4960,3600,3400,1260,1165,139.4K, 0,0,0,40,85,150.1K, 0,40,0,60,300,151.5K, 0,80,60,240,365,150.4K, 80,360,220,380,555,150.4K, 320,360,480,520,530,150K, 360,560,640,440,590,147.4K, 680,480,660,460,515,148.9K, 720,360,780,500,630,148.2K, 1000,560,860,420,605,148.1K, 1280,560,1020,480,700,147.5K, 1680,640,1180,600,660,146.6K, 2240,600,1080,560,585,147K, 0,0,0,0,0,143.9K, 0,360,0,1200,4155,140.9K, 0,3120,40,4360,7045,137.6K, 0,11.44K,640,7060,9300,129.3K, 720,38.88K,5480,10.9K,11.29K,108.1K, 4320,61.24K,12K,12.26K,12K,88K, 5120,66.08K,14.68K,13.5K,12.57K,80.08K, 7080,72.24K,16.62K,12.84K,12.65K,72.11K, 9040,76.44K,19.24K,13.34K,12.33K,66.14K, 13.08K,85.96K,25.06K,14K,12.63K,50.48K, 18.76K,94.64K,28.78K,14.76K,12.91K,36.89K, 27.16K,105.5K,35.46K,15.16K,13.2K,14.53K, 35.12K,113K,39.6K,15.08K,12.72K,0, 0,200,0,940,3220,142.4K, 0,2240,60,3000,5565,139K, 0,9000,440,5400,6865,132.9K, 640,28.6K,4220,8440,8635,115.8K, 3360,44.04K,10.02K,9200,9005,99.79K, 4880,49.12K,11.42K,9180,8635,95.33K, 5680,52.6K,13.92K,9920,8945,89.27K, 6760,57.04K,15.36K,9800,9210,83.93K, 12K,63.56K,19.54K,10.1K,9275,71.77K, 16.04K,68.88K,24.06K,10.42K,9610,62.75K, 24.12K,75.88K,27.68K,10.68K,9600,45.33K, 29.32K,80.84K,30.74K,10.08K,9335,33.32K, 0,200,0,900,2830,143.2K, 0,2080,80,2500,4880,140K, 0,7240,400,4740,6325,134.5K, 760,25K,4000,7300,8155,119.6K, 2880,39.32K,9460,8720,8025,103.3K, 4360,42.04K,11.52K,9000,8045,99.33K, 5880,44.92K,12.68K,9140,8275,94.08K, 6920,48.8K,14.92K,9260,8300,89.78K, 10.12K,54.08K,18.3K,9580,8500,79.72K, 14.92K,58.48K,22.34K,9740,8460,69.7K, 22.76K,63.92K,26.18K,10.32K,8540,54.5K, 28.56K,65.88K,29.36K,9920,9075,44.07K, 0,200,0,500,1985,143K, 0,1680,60,1860,3305,139.8K, 0,5520,640,3460,4040,137K, 640,18.04K,2540,5200,4925,126.1K, 2600,26.6K,6380,5500,5250,116.6K, 2760,28.76K,8280,6460,5305,113.5K, 4200,31.84K,8800,5940,5140,111.4K, 5520,32.28K,11.22K,6020,5550,104.4K, 8400,35.84K,12.76K,6400,5295,98.54K, 11.4K,38.64K,16.74K,5820,5650,92.54K, 16.88K,40.84K,19.34K,6420,5790,82.13K, 20K,43.04K,21.48K,6200,5580,74.38K, 0,0,0,60,660,145K, 0,320,0,400,980,143.9K, 0,1320,60,960,1375,141.9K, 80,3640,1120,1500,1695,139.1K, 840,5320,2100,1780,1695,136.7K, 880,6040,2780,1760,1760,134.9K, 1680,5800,3340,1680,1890,133.9K, 1720,6400,3740,1840,1760,133.4K, 3000,7000,3760,1660,1790,130.5K, 4120,6920,4940,2060,1900,130.5K, 6440,7640,5500,1920,1775,127.3K, 7360,7640,6220,2060,1725,124.2K, 0,0,0,100,325,145K, 0,80,20,280,610,143.8K, 0,520,60,600,670,144K, 0,1640,760,820,1035,141.5K, 400,2280,1660,1040,1145,139.9K, 800,2560,1460,980,1190,138.4K, 920,2560,2080,980,1195,139.7K, 1240,2840,2280,1000,1210,138.6K, 2120,3160,2440,1120,1100,136.3K, 2800,3360,2540,920,1130,135.6K, 3440,3720,3240,1160,1070,134.5K, 4800,3840,3380,920,1135,134.3K, 0,0,0,0,120,141.9K, 0,0,0,120,215,143.1K, 0,80,60,220,335,142.5K, 120,160,380,420,540,143.3K, 240,520,500,380,490,141.9K, 360,720,640,380,545,143.7K, 640,400,640,480,530,141.4K, 480,600,640,480,575,141.7K, 800,400,860,600,640,142.1K, 1200,520,860,560,565,141K, 1800,560,1040,640,540,140.2K, 2120,760,1000,500,590,139.8K, 0,0,0,0,0,135.6K, 0,280,0,1200,4420,134.3K, 0,3560,60,3440,6900,130.4K, 0,10.56K,520,6900,8955,121.8K, 480,36.36K,4480,11K,11.42K,101.1K, 3160,57.4K,10.92K,12.04K,11.95K,81.8K, 4400,64.88K,12.8K,12.74K,12.68K,75.8K, 6120,68.56K,15.46K,13.68K,12.47K,68K, 7480,74.12K,18.14K,13.1K,12.6K,61.27K, 11.04K,84.16K,21.7K,14.14K,12.9K,48.12K, 15.08K,92.76K,27.34K,14.26K,12.58K,35.04K, 23.92K,103.6K,33.96K,14.42K,12.95K,13.2K, 32.4K,110.4K,36.72K,15.06K,13.39K,0, 0,160,0,960,3170,133.5K, 0,2080,40,2680,5470,130.4K, 40,7960,320,5480,6695,126.3K, 680,27.08K,4220,8220,8245,110.3K, 2360,41.72K,9080,9260,8860,94.02K, 3440,46.56K,10.82K,9620,8925,89.22K, 4400,51.28K,12.74K,9400,8995,83.86K, 6600,56.08K,14.48K,9680,8780,79.01K, 9920,61.08K,17.56K,10.3K,8875,69.19K, 14.48K,67.68K,21.22K,10K,9085,57.87K, 21.2K,74.4K,26.18K,10.46K,9245,41.55K, 25.56K,78.64K,30.14K,10.68K,9630,31.09K, 0,200,0,780,3015,133.3K, 0,1960,60,2620,4750,131.7K, 0,6880,360,4300,6335,124.5K, 480,24.12K,3780,7180,7490,113.8K, 2760,37.84K,8100,7880,8105,98.09K, 3800,40.84K,9980,8680,7950,94.3K, 5040,43.96K,11.54K,8700,8125,88.17K, 6000,47.24K,14.44K,8680,8365,84.24K, 9680,51.76K,16.58K,9420,8595,75.01K, 13.48K,57.16K,20.74K,9240,8785,65.9K, 19.88K,61.8K,25.54K,9960,8820,51.27K, 23.64K,65.12K,27.38K,10.16K,8815,41.74K, 0,280,0,360,1920,135K, 0,1880,40,1740,3165,132.3K, 0,5320,360,3480,4000,130.2K, 400,17.6K,2420,4880,4620,120.3K, 2160,26.12K,6100,5380,4965,109.5K, 2600,27.44K,6820,5880,5225,107.5K, 3360,30.6K,8900,5480,5295,104.3K, 4480,31.52K,10.42K,6260,5325,100.5K, 6600,35.72K,12.8K,6060,5395,92.82K, 8880,37.56K,14.8K,6020,5665,87.25K, 14.48K,40.52K,18.56K,6020,5500,76.94K, 19.12K,42.28K,19.18K,6240,5690,71.61K, 0,0,0,140,530,135.9K, 0,320,0,440,990,134.7K, 0,1080,60,940,1265,134.7K, 120,3840,900,1480,1550,133.1K, 840,5160,2020,1820,1635,127.7K, 920,5560,2560,1840,1770,127.7K, 1560,6080,2940,1660,1730,127.6K, 1440,6360,3100,1760,1730,124.7K, 2760,6800,3880,1920,1720,124.8K, 3320,7280,4200,1740,1740,121.4K, 5040,7600,5180,1940,1875,118.7K, 6800,7560,5620,1980,1800,117.4K, 0,0,0,20,345,136.6K, 0,160,0,280,555,135.9K, 0,480,120,540,730,134.5K, 160,1720,460,780,955,134.5K, 480,2480,1400,860,1135,132.6K, 480,2520,1720,900,1125,131.8K, 640,2840,1800,1040,1095,130.7K, 960,3000,1800,880,1190,130.9K, 1440,3360,2300,900,1145,128.5K, 2160,2960,2580,1120,1140,128.6K, 3640,3320,2740,1100,1260,126K, 4320,3560,3140,1060,1235,126.4K, 0,0,0,0,145,135.7K, 0,0,0,80,205,137K, 0,40,40,220,345,135K, 80,160,260,300,475,136.9K, 320,440,500,460,520,133.8K, 160,520,720,360,620,134.3K, 480,360,580,540,575,134.1K, 400,600,700,440,535,134K, 920,400,840,520,565,133K, 1040,560,640,620,560,132.1K, 1600,720,940,500,665,131.4K, 1960,600,1060,560,630,132.1K, 0,0,0,0,0,127K, 0,240,0,740,4250,125.5K, 0,2200,40,3580,6870,120.9K, 40,9320,280,6780,8690,114.1K, 440,33.88K,3960,11.2K,11.15K,95.34K, 2880,55.44K,8940,12.3K,11.88K,76.57K, 3800,60K,11.44K,13.1K,12.05K,69.88K, 4960,65.6K,13.58K,12.74K,12.39K,64.72K, 6160,71.64K,15.6K,12.76K,12.46K,58.15K, 9280,82.12K,20.98K,13.44K,12.6K,44.79K, 14.04K,87.88K,24.96K,13.82K,13.29K,31.51K, 21.84K,98.12K,31.18K,14.64K,12.91K,12.86K, 26.2K,107K,35.28K,15.32K,13.04K,0, 0,0,0,740,3140,127.4K, 0,1960,80,2540,5075,122.5K, 40,7680,200,4780,6675,117.9K, 360,23.8K,3480,8220,8210,103.4K, 2160,40.68K,7360,8940,8645,88.43K, 2840,45.44K,9180,9440,8645,83.78K, 4040,48.68K,11.74K,9720,9075,78.74K, 5360,52.48K,13.52K,9500,9270,73.72K, 8480,59.88K,15.86K,9260,8935,63.39K, 11.4K,64.84K,20.16K,10K,8835,53.87K, 17.24K,71.84K,24.54K,10.44K,9340,39K, 22.92K,75.48K,27.52K,10.92K,9340,29.29K, 0,120,0,660,2820,126.2K, 40,1600,40,2520,4625,123.2K, 0,6480,380,4300,6125,119.2K, 560,22.32K,3300,7500,7360,105.6K, 2320,35.92K,7660,8340,7695,92.7K, 3280,38.32K,9060,8200,8190,87.59K, 4120,41.68K,10.74K,8940,8135,83.63K, 5520,45.44K,12.88K,8120,8535,78.07K, 7200,50.24K,15.76K,9240,8500,70.23K, 10.68K,55.68K,18.64K,9300,8835,61.06K, 17.32K,59.04K,23.3K,10.2K,8755,48.33K, 21.2K,63.68K,25.38K,9660,8830,39.17K, 0,160,0,340,1965,125.6K, 40,1400,40,1920,3085,124.5K, 0,4720,340,3040,4190,121.3K, 280,15.76K,2360,5080,4705,111.6K, 1760,24.68K,5380,5400,5275,103.3K, 2360,26.92K,6540,5680,5225,99.92K, 3400,29.36K,7660,5560,5200,96.88K, 3680,30.96K,9120,5840,5320,93.81K, 5960,34.44K,10.72K,5600,5485,88.2K, 8240,36.28K,13.94K,6080,5620,80.22K, 11.64K,39.72K,17.34K,6420,5480,71.2K, 15.16K,41.44K,19.02K,6720,5395,65.96K, 0,0,0,120,560,127.6K, 0,280,0,460,960,126.1K, 0,1120,120,760,1235,126.4K, 160,3440,780,1440,1750,121.5K, 720,5080,2000,1460,1730,121.1K, 640,5280,2400,1760,1840,119.9K, 960,5720,2780,1700,1835,119.4K, 1320,6240,2880,1740,1775,117.2K, 2200,6280,3840,2000,1775,116.7K, 3120,7200,4280,2000,1680,114.4K, 5280,7600,5060,1960,1575,110.8K, 5920,7400,5480,2020,1895,109.5K, 0,0,0,60,310,126.5K, 0,80,0,320,620,126K, 0,400,80,540,700,126.1K, 40,1760,320,660,1060,123.4K, 480,2400,960,880,1060,122.8K, 400,2560,1280,800,1210,122.8K, 800,2760,1420,1000,1245,120.8K, 880,2960,1680,840,1070,120.6K, 1160,2920,2520,1160,1080,121.8K, 2040,3000,2680,1060,1185,120.5K, 3080,3240,2540,1200,1160,118.2K, 3400,3440,3160,1080,1170,116.6K, 0,0,0,40,140,127.6K, 0,0,0,40,235,128.1K, 0,40,20,180,315,126.4K, 0,160,320,300,475,125.1K, 360,600,260,220,575,126.5K, 200,280,500,480,550,125.4K, 320,520,520,400,540,125.5K, 480,320,680,560,560,125.3K, 720,520,760,500,500,123.5K, 880,560,880,480,550,124.4K, 1280,840,860,500,640,124.3K, 1680,600,900,520,555,122.8K, 0,0,0,0,0,118.7K, 0,240,0,800,3870,115.9K, 0,2120,0,3300,6650,111.5K, 0,8560,300,6620,8330,107.5K, 480,31.12K,3280,10.74K,11.14K,88.28K, 2240,51.64K,8260,12.18K,11.85K,69.9K, 3600,56.84K,10.08K,12.44K,11.99K,64.45K, 3760,62.56K,12.68K,12.96K,12.04K,58.19K, 4600,66.52K,14.38K,13.14K,12.18K,53.54K, 8080,77.16K,18.34K,13.38K,12.64K,40.8K, 11.88K,85.04K,21.46K,14.48K,12.93K,30.01K, 19K,95.76K,27.38K,15.1K,12.93K,11.56K, 22.72K,102.5K,30.36K,15.28K,12.84K,0, 0,40,0,660,2915,115.3K, 0,1600,100,2400,5110,114.2K, 40,6360,240,4840,6580,110.5K, 440,23.56K,2720,8120,7915,95K, 1640,38.96K,7220,8520,8890,82.42K, 2360,43.12K,8540,9220,8790,77.34K, 3200,45.52K,10.58K,9260,8925,72.32K, 4440,49.96K,12.34K,9820,8985,68.78K, 6240,56.52K,14.96K,9960,8935,58.76K, 9600,61.92K,18.64K,9260,8990,49.76K, 15.36K,69.4K,21.84K,10.34K,9220,36.2K, 18.56K,73.68K,24.96K,10.7K,9780,27.68K, 0,40,0,620,2620,118K, 0,1360,20,2360,4590,115.2K, 0,5680,200,4820,5805,110.8K, 400,21.44K,2340,6600,7065,97.72K, 1600,33.44K,6320,7580,7985,85.33K, 2200,35.96K,8260,8940,7825,80.88K, 3160,39.96K,9720,8600,8155,76.16K, 4360,43.24K,11.38K,8700,8145,74.58K, 6120,48.6K,14.46K,9320,8490,65.15K, 9360,53.92K,16.68K,9320,8425,56.26K, 14.92K,58.24K,21.08K,9040,8845,44.91K, 18.88K,62.64K,23.34K,9300,8635,36.65K, 0,200,0,200,1810,118K, 0,1080,0,1660,3015,116K, 40,3840,240,3560,4065,112.2K, 520,15.2K,1540,4760,4870,103.1K, 1040,23.6K,4760,5240,5025,95.53K, 2160,25.48K,5880,5460,5170,92.39K, 2400,28.28K,6780,5540,4925,89.88K, 3800,29.52K,7580,5900,5210,86.84K, 4520,32.88K,10.38K,6080,5285,81.03K, 7480,35.76K,12.54K,5800,5365,75.96K, 10.96K,38.32K,15.7K,6440,5550,65.74K, 13.88K,40.28K,17.92K,6320,5390,60.61K, 0,0,0,160,565,118K, 0,200,20,540,900,116K, 0,1080,20,920,1150,116.2K, 120,2960,720,1480,1575,114.6K, 560,4440,1880,1700,1735,113K, 640,5440,1840,1540,1830,111.2K, 800,5520,2540,1920,1755,110.2K, 1120,6160,2520,1480,1795,109.8K, 1560,6400,3720,1860,1725,108.6K, 2560,7040,4140,1760,1840,105.6K, 4440,7440,4540,1700,1770,104.1K, 5120,7640,4800,2240,1895,101.9K, 0,0,0,20,295,117.6K, 0,80,0,220,635,117.4K, 0,280,60,380,825,117.7K, 0,1600,560,760,1030,117.5K, 320,2360,920,920,965,114.2K, 560,2480,1160,780,1095,114.1K, 360,2680,1500,1000,1035,112.8K, 680,2720,1720,760,1205,113.4K, 1040,2680,2000,1140,1205,113.1K, 1680,3080,2020,1140,1060,112.2K, 2640,3280,2520,1100,1205,110.6K, 3240,3440,3100,1020,1135,108.6K, 0,0,0,0,90,118.9K, 0,40,0,40,195,118.2K, 0,40,20,120,305,118.1K, 80,320,220,220,450,118.1K, 160,440,400,380,520,115.8K, 240,400,380,480,500,116.6K, 520,360,400,500,535,117.4K, 440,400,480,480,550,117.2K, 560,600,780,460,540,115.2K, 760,520,780,520,535,116.7K, 1040,760,940,400,640,113.8K, 1440,560,1020,660,595,114K, 0,0,0,0,0,110K, 0,200,0,600,3590,107.9K, 0,1680,40,2900,6320,104.5K, 0,7320,260,5860,8465,97.96K, 280,28.88K,3200,9780,11.03K,81.57K, 1280,47.24K,7300,11.78K,11.89K,65.65K, 2040,53.04K,9340,12.14K,11.42K,59.57K, 3040,58.52K,11.56K,12.54K,12.53K,54.41K, 3840,63.52K,12.4K,12.4K,12.24K,50.13K, 6640,71.2K,16.5K,14.1K,12.55K,38.91K, 9400,80.36K,20.38K,13.88K,12.91K,27.87K, 15.28K,90.88K,25.08K,13.66K,12.7K,11.01K, 17.64K,98.28K,28.64K,14.44K,13.09K,0, 0,160,0,560,2620,108.5K, 0,1240,60,2260,4635,106K, 0,5400,240,4580,6450,100.7K, 120,21.8K,2520,7840,7870,88.36K, 1400,36.04K,6120,9020,8490,75.05K, 2400,40.92K,7600,9460,8480,72.21K, 3000,42.96K,9280,9320,8785,67.04K, 3480,47.08K,10.72K,9420,8770,63.1K, 5440,52.88K,13.06K,9680,9175,54.62K, 7840,58.32K,16.3K,9900,9055,45.78K, 12.64K,65.44K,20.8K,10.34K,9155,32.9K, 15.96K,70.4K,22.7K,10.36K,9560,25.24K, 0,120,20,560,2420,107.8K, 0,1320,0,2000,4430,104.9K, 40,5080,320,4280,5310,101.6K, 240,19.12K,1920,7080,7210,89.96K, 1360,31.2K,5760,7640,7720,78.9K, 1800,33.48K,7260,8960,8035,75K, 2720,38.2K,8460,8720,7790,70.9K, 3720,39.92K,9820,8620,8400,67.52K, 5640,45.96K,12.44K,8920,8090,60.06K, 7280,50.84K,14.42K,9480,8665,52.51K, 12.6K,55.88K,19.02K,9200,8710,40.68K, 15.6K,58.88K,21.74K,9620,8665,33.59K, 0,40,0,380,1690,107.9K, 0,920,0,1240,2835,106.3K, 0,3760,100,3120,3900,103.3K, 200,14.72K,1680,4620,4540,96.17K, 1240,21.68K,4340,5260,4845,89.27K, 1520,23.64K,5400,5620,5435,86.71K, 1920,25.84K,6660,5420,5130,82.75K, 2240,27.84K,7200,5800,5215,80.77K, 4400,31.88K,9200,5640,5470,74.56K, 5520,33.2K,11.42K,6300,5535,69.62K, 8040,36.24K,14.14K,6720,5585,62.12K, 11.12K,38.36K,15.76K,6260,5485,55.76K, 0,0,0,120,520,110K, 0,80,0,380,910,109.7K, 0,1040,100,800,1090,108.3K, 120,2960,640,1320,1625,105.6K, 360,4720,1500,1280,1720,103.4K, 720,4960,1640,1900,1780,102.9K, 520,5080,1780,1840,1815,101.3K, 520,5880,2620,1520,1805,101.9K, 1440,6200,3200,1800,1895,99.42K, 1960,6800,3700,1880,1680,98.92K, 2840,7040,4460,1920,1815,96.36K, 4560,7880,4620,1900,1690,94.01K, 0,0,0,80,200,109.4K, 0,40,20,280,450,110.1K, 0,280,20,400,600,107.4K, 80,1440,400,720,930,107K, 320,2160,920,760,1065,105.9K, 280,2320,1120,980,1050,106.3K, 560,2280,1400,980,1245,104.2K, 400,2800,1540,820,1130,103.3K, 840,2720,1720,1140,1170,104.8K, 1280,3040,2220,1040,1065,103K, 2120,3440,2420,1020,1060,102K, 2280,3200,2840,1080,1100,100.2K, 0,0,0,0,75,108.5K, 0,0,0,40,215,109.7K, 0,0,80,100,320,109.7K, 40,120,140,420,455,108.4K, 120,600,260,280,430,108.4K, 120,480,420,320,485,107K, 360,320,420,480,595,106.2K, 360,440,640,340,625,107.2K, 600,360,660,540,555,106.2K, 600,640,780,400,645,108.2K, 1040,560,800,680,570,107.4K, 1000,600,1120,580,510,106.1K, 0,0,0,0,0,100.2K, 0,120,0,560,3550,98.01K, 0,1200,40,2520,6345,94.63K, 0,6360,160,5660,8375,91.49K, 280,25.6K,2540,9880,11.01K,75.46K, 1240,44.16K,5820,11.7K,11.69K,60.68K, 1840,49.72K,7460,11.76K,11.97K,55.24K, 2360,55.28K,9400,12.14K,11.9K,50.81K, 3360,58.2K,10.96K,12.8K,12.24K,46.05K, 5520,66.4K,14.82K,13.52K,12.55K,35.16K, 8120,75.84K,18.16K,13.54K,12.54K,25.3K, 11.2K,85.96K,23.52K,14.56K,12.85K,9485, 16.44K,93.4K,25.92K,14.36K,12.89K,0, 0,40,0,500,2515,99.5K, 0,1240,60,1920,4700,96.59K, 0,4760,180,3820,6480,93.82K, 280,20.08K,2080,7480,7655,80.97K, 1120,33.08K,5680,8520,8470,69.87K, 1440,37.32K,6980,9100,8710,65.59K, 2320,39.68K,7460,8420,8650,62.21K, 2880,43.96K,9400,8740,9025,58.73K, 4840,50.32K,11.5K,9480,8860,49.49K, 6400,56K,14.98K,9760,9140,42.63K, 10.88K,62.8K,17.86K,10.14K,9135,31.41K, 13.08K,68.32K,20.84K,10.18K,9665,23.5K, 0,40,0,520,2410,99.39K, 0,1120,20,2200,3955,98.12K, 0,4160,140,4140,5515,94.78K, 120,17.64K,2060,6180,7190,83.98K, 600,28.04K,5340,7900,7780,71.86K, 1440,32.68K,5680,8120,8255,69.47K, 2560,35.6K,7240,8540,7765,65.99K, 2480,37.68K,8440,8260,8200,63.51K, 4240,44.52K,11.12K,8780,8370,55.74K, 6000,47.6K,14.52K,9000,8395,48.08K, 10.04K,53.76K,17.62K,9360,8875,37.66K, 12.88K,57K,19.2K,9720,8715,31.34K, 0,40,0,320,1680,100.1K, 0,680,0,1420,2780,98.26K, 0,3160,200,3180,3645,95.03K, 240,12.44K,1420,4820,4530,89K, 760,20.2K,4000,5040,4950,81.92K, 1040,22.52K,4100,5380,4995,79.1K, 1440,23.96K,5900,5620,4830,75.98K, 2080,26.68K,6480,5940,5210,73.13K, 2920,29.52K,8160,6280,5105,69.29K, 4320,32.44K,10.66K,5580,5510,64.33K, 7080,35.24K,12.98K,5880,5665,57.29K, 9720,38.32K,14.52K,5880,5445,52.89K, 0,0,0,40,405,101.6K, 0,120,20,300,935,99.96K, 0,720,40,760,1075,100.6K, 160,2800,480,1300,1575,98.59K, 200,4640,1420,1300,1635,95.2K, 160,5040,1860,1540,1655,94.29K, 560,5200,2140,1560,1640,93.87K, 800,5600,2100,1700,1700,93.63K, 1320,6360,2820,1560,1670,91.95K, 1880,6360,3060,2020,1680,90.94K, 2480,6760,3700,2060,1720,87.77K, 3600,7120,4280,1740,1835,86.52K, 0,0,0,80,235,101.9K, 0,0,0,200,475,100.7K, 0,200,40,380,730,100.9K, 0,1440,260,640,855,99.05K, 200,2160,840,680,935,97.05K, 200,2280,1040,940,1070,96.94K, 360,2200,1140,1020,1060,96.94K, 680,2480,1320,860,1150,97.47K, 920,2840,1460,920,1085,95.39K, 880,2880,1980,1020,1150,94.84K, 1840,3240,2200,900,1150,94.52K, 2160,3160,2700,1100,1300,92.72K, 0,0,0,0,85,101.9K, 0,0,0,40,170,100K, 0,40,20,80,255,101.6K, 0,200,100,220,520,101.3K, 120,320,300,360,570,100.2K, 120,440,480,280,515,100.6K, 160,480,460,260,620,100.5K, 400,520,720,360,550,99.39K, 440,400,640,460,620,97.38K, 720,440,600,520,605,98.66K, 880,680,800,520,545,98.31K, 1120,560,880,460,550,98.13K, 0,0,0,0,0,94.7K, 0,120,0,460,3285,90.98K, 0,1120,20,2540,5890,87.49K, 0,5840,160,5360,7990,84.13K, 160,23.52K,1900,9160,10.2K,68.91K, 1120,40.04K,5360,11.56K,11.46K,55.74K, 1240,45.24K,6660,11.78K,12.15K,52.26K, 1920,50.48K,8580,12.08K,11.95K,45.86K, 2280,54.76K,10.54K,12.54K,11.77K,42.5K, 3960,64K,13.22K,12.56K,12.04K,32.99K, 6920,71.32K,16.2K,13.2K,12.52K,23.48K, 9920,81.84K,20.16K,13.76K,12.53K,9520, 12.8K,87.36K,24.64K,14.7K,12.94K,0, 0,80,0,520,2280,91.69K, 0,1080,0,1740,4325,89.69K, 0,4280,200,4360,5910,86.11K, 280,17.92K,1720,6940,7800,75.41K, 920,31K,4540,8040,8495,64.59K, 1520,34.2K,5240,8480,8875,60.64K, 1400,38.36K,6720,8560,8535,56.42K, 2200,41.56K,7720,8700,8925,54.64K, 3320,47.68K,10.74K,9940,8960,47.22K, 5240,52.04K,13.62K,10.26K,9070,39.13K, 8680,59.32K,16.56K,10.08K,9555,28.43K, 10.64K,64.64K,19.4K,10.22K,9160,21.77K, 0,0,0,420,2205,90.58K, 0,800,40,1760,3885,89.13K, 0,3480,120,3480,5510,86.38K, 120,15.8K,1780,6200,7110,76.48K, 800,26.76K,4360,7420,7670,66.51K, 1480,29.88K,5760,7900,7865,64.46K, 1640,33.12K,6180,7980,7770,61.36K, 1840,36.08K,8080,8120,8035,57.11K, 3360,41.68K,10.46K,8700,8145,52.25K, 4800,45.52K,12.24K,8720,8400,44.28K, 8280,51.2K,15.34K,9340,8390,35.1K, 11.52K,54.96K,17.9K,9320,8460,28.18K, 0,40,0,240,1500,91.94K, 0,560,60,1220,2630,90.81K, 0,2680,160,2480,3760,88.51K, 160,11.32K,1020,4660,4480,81.97K, 480,19.24K,3040,4760,4680,74.82K, 520,20.76K,3760,5360,5260,71.92K, 1360,22.92K,4720,5380,5010,69.56K, 1920,24.52K,5500,5740,4975,68.09K, 2440,27.4K,7140,5640,5360,62.99K, 4360,29.56K,9000,6360,5635,60.26K, 6320,34.68K,12.24K,6000,5355,52.48K, 7160,36.2K,13.46K,6720,5540,48.42K, 0,0,0,80,390,92.99K, 0,80,0,340,865,92.02K, 0,600,60,720,1050,92.61K, 0,2560,400,1300,1580,90.67K, 240,4240,1240,1340,1700,87.35K, 360,4640,1460,1500,1595,88.14K, 240,4840,1980,1600,1635,86.52K, 720,4760,2100,1840,1765,86.47K, 1120,5520,2660,1920,1790,86.24K, 1360,5880,3100,2060,1830,82.48K, 2560,6680,3720,2140,1585,82.04K, 3360,6960,4240,1840,1730,79.91K, 0,0,0,0,310,92.98K, 0,40,0,100,425,90.84K, 0,320,20,300,710,92.48K, 0,1320,260,660,875,91.29K, 120,1840,700,720,1110,89.91K, 240,2240,960,700,1075,89.65K, 360,2280,1120,880,1090,88.03K, 600,2320,1060,960,1145,90.12K, 680,2640,1560,940,1060,88.17K, 880,2800,1740,1140,1130,88.64K, 1240,3120,2220,1080,1145,87.13K, 1680,3000,2620,1140,1160,86.26K, 0,0,0,0,100,92.67K, 0,0,0,20,190,93.83K, 0,40,20,100,315,93.14K, 40,160,100,320,410,93.04K, 40,360,300,220,565,92.24K, 160,360,220,380,550,92.53K, 80,360,500,400,475,91.86K, 160,400,400,400,595,92.21K, 160,560,500,460,480,91.6K, 480,520,520,420,610,90.5K, 880,480,560,480,505,91.14K, 1120,520,920,540,525,90.14K, 0,0,0,0,0,87.13K, 0,80,0,440,3320,84.82K, 0,1120,20,2280,5725,82.08K, 0,4680,160,4880,7995,78.61K, 120,20.36K,1940,9800,10.21K,65.28K, 760,37.2K,4480,10.82K,11.41K,52.47K, 960,42.24K,5840,11.54K,11.7K,46.88K, 1920,46.32K,7140,11.86K,11.67K,43.34K, 2400,51.68K,8840,12.06K,11.78K,39.38K, 3720,59.76K,11.4K,12.76K,12.41K,30.28K, 4880,67.88K,14.82K,13.08K,12.49K,21.74K, 8720,77.68K,18.48K,13.46K,12.28K,8770, 10.64K,82.88K,22.14K,14.22K,12.72K,0, 0,0,0,360,2315,85.59K, 0,680,40,1760,4225,82.96K, 0,3920,100,3860,5760,79.99K, 80,15.92K,1440,6980,7695,71.23K, 400,28.48K,3700,7900,8595,59.1K, 920,32.04K,5400,8180,8630,56.58K, 1360,35.6K,5900,8540,8995,52.91K, 1880,39.24K,7160,8480,8745,50.12K, 2760,45.16K,10.08K,9420,8630,43.4K, 3960,48.68K,12.48K,9700,9095,36.87K, 6680,58.28K,15.16K,9240,9015,26.78K, 9240,61.52K,17.14K,10.24K,9230,20.31K, 0,40,0,360,2055,84.5K, 0,680,0,1160,3870,83.24K, 40,3200,120,3280,5275,80.68K, 120,14.36K,1280,6020,7290,71.53K, 640,24.08K,3920,8280,7480,62.68K, 1320,29.16K,4560,7440,7750,58.91K, 1200,31.4K,5800,7880,8100,56.4K, 1800,32.68K,6900,8320,7845,54.32K, 2440,38.56K,9280,8420,8635,47.09K, 4240,42.28K,11.24K,8640,8305,42.27K, 5840,48.28K,15.04K,9360,8850,32.95K, 9280,52.52K,16.12K,9120,8695,26.53K, 0,40,0,220,1360,84.46K, 0,480,0,1080,2520,84.14K, 0,2520,100,2320,3635,82.56K, 200,10.44K,880,4340,4520,76.97K, 520,18.16K,2500,4880,4885,69.42K, 840,20.24K,3140,5280,4925,68.05K, 1040,21.36K,4280,5380,4910,66.23K, 1640,23.36K,4260,5360,5465,63.8K, 2080,25.96K,6420,5660,5350,59.11K, 3360,28.6K,8720,5900,5410,54.8K, 5400,32.6K,10.66K,6540,5450,48.94K, 6440,34.72K,12.46K,5960,5335,45.43K, 0,0,0,60,500,85.87K, 0,40,0,240,815,86.8K, 0,480,20,700,1085,84.15K, 0,2200,300,1240,1540,83.94K, 40,3960,1020,1320,1615,82.63K, 160,4160,1280,1460,1675,81.49K, 440,4640,1320,1380,1795,79.54K, 560,4840,1480,1700,1650,80.74K, 840,5480,2520,1780,1725,78.66K, 880,6040,3380,1620,1710,77.8K, 2240,6440,3560,1700,1600,75.49K, 2280,6560,3820,1760,1785,74.93K, 0,0,0,0,210,86.35K, 0,0,0,200,350,85.68K, 0,200,0,320,610,86.34K, 0,920,220,720,915,84.05K, 40,1880,640,780,945,83.34K, 80,2040,740,740,1050,83.63K, 280,2200,860,880,1060,83.73K, 320,2320,1020,640,1115,82.78K, 480,2360,1360,1060,995,82.07K, 640,2920,1700,820,1085,80.82K, 960,3120,2300,1060,1080,81.62K, 1520,3080,2100,1120,1045,79.66K, 0,0,0,0,95,87.14K, 0,0,0,20,195,85.91K, 0,40,0,120,310,86.01K, 0,120,100,260,465,86.1K, 80,120,320,320,550,85.94K, 120,240,300,360,525,84.64K, 160,440,240,420,500,85.62K, 160,440,360,440,525,85.95K, 240,520,580,340,500,85.7K, 280,520,520,480,520,85.57K, 520,360,600,560,635,83.92K, 800,440,700,560,575,84.78K, 0,0,0,0,0,80.56K, 0,80,0,480,2790,80.08K, 0,840,60,2240,5465,77.15K, 0,4880,120,4360,7885,73.38K, 160,18.88K,1360,8520,10.29K,61.62K, 440,34.96K,3940,11.08K,10.95K,48.62K, 920,39.84K,5020,11.3K,11.4K,44.56K, 1040,44.12K,6300,11.78K,11.45K,41.1K, 1560,48.16K,8200,11.3K,12.1K,36.29K, 2880,56.36K,10.06K,12.44K,11.67K,28.62K, 3840,63.6K,12.82K,13.26K,12.37K,20.31K, 6960,72.68K,17.86K,13.62K,12.8K,8150, 8680,78.36K,20.28K,14.52K,12.93K,0, 0,160,20,240,2165,79.71K, 0,640,0,1600,4155,79.45K, 0,3280,20,3540,5890,75.96K, 200,14.6K,1020,7060,7215,65.52K, 400,27.28K,3300,7680,8330,56.69K, 600,29.44K,4640,8140,8405,53.49K, 1520,32.68K,5440,9200,8550,50.26K, 1280,35.88K,6440,9080,8655,46.51K, 2680,41.32K,8200,9380,8670,40.97K, 3200,47.08K,10.44K,9800,8910,33.86K, 6120,54.56K,13.04K,9480,9260,24.79K, 8360,58.4K,15.9K,10K,9540,18.87K, 0,40,0,280,2010,78.91K, 0,480,20,1160,3740,78.81K, 0,2720,140,3100,4945,75.67K, 40,12.96K,1300,6060,6935,67.96K, 480,23K,3080,7420,7255,59.26K, 720,26.36K,4120,7140,7930,55.67K, 1040,29.32K,4940,7520,7745,52.55K, 1360,31.2K,6080,7640,7995,50.33K, 2680,35.72K,8620,8600,8365,44.75K, 3960,40.68K,9860,8800,7960,39.17K, 5520,47.28K,12.62K,8840,8260,31K, 6800,50.04K,15.46K,8920,8720,25.58K, 0,0,0,280,1140,80.15K, 0,400,20,940,2650,79.87K, 0,1880,100,2360,3665,78K, 40,9240,780,4820,4205,70.81K, 440,16.44K,2620,4720,4710,65.67K, 1120,18.32K,2960,5360,4875,63.08K, 1000,20.16K,3860,5420,5005,62.88K, 1320,21.68K,4340,5460,5145,60.16K, 1880,24.48K,5640,5680,5280,55.89K, 2600,27.8K,7160,5940,5345,51.16K, 4160,32.16K,10.12K,5740,5435,46.16K, 5920,33.6K,10.38K,5920,5315,42.69K, 0,0,0,20,460,81.17K, 0,80,0,280,735,81.14K, 0,320,0,620,1005,81.04K, 0,1880,320,1320,1390,78.96K, 40,3520,900,1520,1570,77.3K, 280,3800,1160,1540,1610,77.09K, 160,4040,1260,1560,1660,76.38K, 320,4960,1660,1440,1765,75.28K, 520,4960,2060,1760,1750,74.67K, 840,5600,2620,1720,1625,71.99K, 1600,6000,2980,1820,1580,72.46K, 2200,6400,3820,1720,1870,69.85K, 0,0,0,0,205,80.05K, 0,0,0,160,385,81.63K, 0,160,0,260,615,80.18K, 0,720,160,640,845,79.63K, 0,1600,620,960,1010,78.63K, 160,1920,680,700,985,77.65K, 280,2200,680,740,1025,78.52K, 160,2200,820,840,1020,78.01K, 320,2440,1420,700,1035,78.12K, 640,2600,1420,820,1115,77.51K, 920,3160,1820,960,1060,76.17K, 1200,3040,2080,1020,1210,75.87K, 0,0,0,0,80,81.9K, 0,0,0,20,160,80.18K, 0,0,0,140,275,81.09K, 0,160,120,220,380,80.43K, 40,200,220,280,465,80.14K, 80,280,320,240,525,80.12K, 80,480,200,400,530,80.39K, 120,320,420,460,540,81.43K, 240,480,500,420,535,78.85K, 400,360,500,440,565,79.71K, 400,640,660,340,545,78.99K, 400,520,740,580,610,79.66K, 0,0,0,0,0,76.22K, 0,40,0,320,2730,76.2K, 0,520,0,2060,5405,72.6K, 0,3800,160,4440,7350,69.03K, 0,18.12K,1260,8120,10.15K,57.06K, 280,32.12K,3640,10.84K,11.03K,46.6K, 640,36.96K,4220,10.98K,11.38K,42.88K, 1040,41.16K,5660,11.14K,11.21K,38.6K, 1840,46.12K,6840,11.68K,11.58K,34.87K, 2560,52.44K,9160,12.44K,12.08K,26.98K, 3920,60.08K,12.2K,12.2K,12.52K,19.11K, 5320,69.8K,15.64K,13.74K,12.98K,7655, 7640,76.2K,18.1K,13.52K,12.43K,0, 0,0,0,220,2090,75.39K, 0,600,20,1280,3920,73.8K, 0,2520,80,3440,5645,69.83K, 80,13.76K,1060,6620,7375,61.19K, 480,25.32K,2500,7920,8155,53.33K, 920,27.16K,4000,8420,8350,49.82K, 760,31.76K,5020,8420,8440,46.72K, 1000,34.12K,6340,8240,8690,43.89K, 2280,40.16K,7620,8460,8540,38.69K, 2840,45.12K,10.52K,8420,9255,32.41K, 5440,51.8K,13.78K,9500,9090,23.6K, 6600,56.24K,15.32K,9880,9285,17.77K, 0,0,0,220,1875,76.16K, 0,480,0,1220,3435,74.14K, 0,2680,80,2880,5110,70.94K, 40,12.28K,1220,5620,6755,63.66K, 480,21.8K,2860,7180,7215,56.3K, 640,24.8K,3700,7360,7285,53.18K, 880,26.88K,4980,7760,8025,49.84K, 1120,29.92K,5400,7580,8045,47.95K, 2080,35K,7600,7900,7920,41.96K, 3080,38.24K,9640,8420,8085,36.86K, 4800,44.84K,12.08K,9060,8370,28.49K, 6680,47.32K,14.6K,9320,8265,23.23K, 0,0,0,180,1235,74.86K, 0,520,20,820,2525,75.26K, 0,1920,60,2020,3495,72.73K, 80,8720,860,4000,4405,67.17K, 240,14.52K,2180,5140,4885,62.45K, 720,16.4K,2560,5320,5005,60.03K, 760,19.96K,3100,5160,4795,58.57K, 720,20.48K,4120,5620,5165,55.78K, 1400,24.24K,5220,5500,4925,52.6K, 2480,26.44K,6860,5420,5270,48.97K, 3440,31.28K,8700,5720,5390,43.59K, 4920,32.28K,10.84K,6280,5145,39.03K, 0,0,0,120,320,76.91K, 0,80,0,220,740,76.07K, 0,400,0,440,1015,74.97K, 0,1800,320,1320,1375,73.57K, 120,3560,940,1380,1515,71.65K, 160,3840,1060,1380,1545,72.16K, 80,4200,1140,1240,1600,72.16K, 160,4640,1440,1620,1595,71.07K, 720,4560,2040,1860,1740,69.98K, 720,5800,2420,1420,1605,69.29K, 1320,6320,2980,1380,1800,67.22K, 1520,6360,3720,1760,1820,66.1K, 0,0,0,0,230,77.41K, 0,0,20,100,345,78.46K, 0,160,40,300,645,76.23K, 0,1000,80,440,765,75.42K, 40,1520,520,720,1060,73.29K, 240,1920,600,760,840,74.67K, 200,1960,500,720,1165,73.12K, 280,2040,880,920,1180,73.09K, 320,2240,1240,820,1185,72.89K, 520,2280,1500,880,1155,73.03K, 1000,2760,1700,1000,1145,71.3K, 1000,2920,1980,1040,1205,70.7K, 0,0,0,20,45,77.38K, 0,0,0,0,150,77.42K, 0,0,0,100,240,76.27K, 0,120,100,300,330,75.88K, 80,320,120,260,435,74.98K, 40,240,260,400,435,76.02K, 80,240,440,460,590,75.96K, 200,240,360,460,510,76.12K, 280,480,360,400,535,76.2K, 360,440,620,400,535,76.04K, 360,480,620,460,520,76.2K, 840,720,580,460,490,75.02K, 0,0,0,0,0,72.24K, 0,80,0,400,2565,70.91K, 0,440,0,1780,4875,69.69K, 0,3240,80,4000,7330,64.97K, 160,16.36K,1040,8160,9880,54.05K, 240,30.68K,3280,9640,11.25K,43.66K, 440,35K,4360,10.36K,11.42K,40.44K, 840,38.8K,5000,11.28K,11.47K,35.59K, 1360,43.08K,5760,11.56K,11.47K,32.63K, 1640,50.72K,8780,12.04K,11.78K,25.42K, 3240,57.08K,10.18K,12.8K,12.31K,18.72K, 4760,67.4K,14.98K,13.08K,12.18K,7125, 6560,73.16K,18.02K,13.22K,12.77K,0, 0,0,0,220,1925,71.49K, 0,480,60,1440,3505,68.88K, 0,2240,40,3220,5745,66.49K, 160,12.24K,800,6540,7265,57.74K, 320,24.16K,2800,7600,7880,50.18K, 520,25.84K,3460,7480,8340,47.62K, 640,29.52K,4520,7920,8490,44.73K, 960,32.24K,4500,8420,8340,41.68K, 1400,38.36K,7340,8360,8840,36.28K, 3080,43.32K,8160,9460,8915,31.17K, 4280,49.48K,12.1K,9240,8850,22.56K, 5840,53.6K,13.28K,10.18K,9285,17.23K, 0,40,0,180,1695,72.32K, 0,360,0,1180,3480,70.03K, 0,2200,60,2760,4890,68.3K, 80,11.92K,840,5500,6705,60.7K, 240,20.48K,2680,6920,7575,51.94K, 560,23.28K,3160,6900,7750,50.44K, 800,25.48K,3660,7660,7850,47.49K, 1280,28.6K,4120,7960,7590,45.26K, 1640,32.48K,6660,8180,7890,40.95K, 2520,37.16K,8580,8460,8320,35.03K, 4480,43.08K,11.32K,8760,8380,27.34K, 5400,45.68K,13.48K,8740,8240,22.98K, 0,40,0,140,1200,71.96K, 0,480,0,880,2480,70.41K, 0,1720,0,1860,3710,68.75K, 120,7800,540,4220,4240,63.56K, 320,14.4K,1640,4800,4610,57.93K, 360,15.6K,2380,5440,5190,56.66K, 560,18.08K,2880,5320,4865,54.96K, 680,19.76K,3260,5420,5255,54.08K, 1480,23.52K,4700,5060,5020,49.02K, 1280,25.48K,6800,5800,5175,46.25K, 3280,29.64K,8380,5520,5345,41.51K, 4320,30.72K,9980,6060,5275,37.42K, 0,0,0,20,370,71.8K, 0,120,0,200,670,72.56K, 0,400,20,500,935,71.33K, 0,1920,360,1060,1420,71.55K, 80,2960,980,1520,1615,69.42K, 160,3680,1000,1300,1665,67.96K, 200,3920,1200,1400,1555,68.12K, 280,4400,1480,1540,1705,67.59K, 400,4640,1520,1740,1750,66.4K, 680,5440,2100,1620,1670,64.69K, 1240,6080,2620,1840,1620,63.59K, 1280,6400,3040,1840,1750,62.94K, 0,0,0,0,185,71.66K, 0,0,0,80,425,72.85K, 0,160,0,300,600,73.09K, 0,1040,100,420,865,71.15K, 40,1320,380,820,915,70.41K, 80,1600,520,980,995,70.39K, 160,1560,720,820,1040,70.6K, 0,1840,840,700,1090,69.34K, 440,2080,1080,780,1100,70.32K, 280,2280,1280,960,1135,68.72K, 520,2640,1780,1000,1175,69.05K, 1160,2840,1720,1100,1090,67.56K, 0,0,0,0,80,72.47K, 0,0,0,0,145,72.8K, 0,0,0,80,255,73.01K, 40,120,100,180,385,72.37K, 160,240,180,380,450,71.6K, 80,280,220,340,470,71.74K, 80,400,220,360,435,71.66K, 200,400,240,260,525,71.69K, 240,240,460,460,510,72K, 280,440,380,420,555,71.67K, 280,720,580,320,610,70.47K, 520,400,720,580,545,71.05K, 0,0,0,0,0,69.27K, 0,0,0,360,2455,68.23K, 0,760,0,1420,4800,66.7K, 0,2920,60,3920,6960,62.85K, 40,15.52K,980,7680,9825,52.18K, 400,28.88K,3300,10.08K,10.91K,41.81K, 280,32.96K,3620,10.34K,11.3K,38.43K, 520,36.64K,4700,10.98K,11.54K,34.78K, 1160,40.84K,5940,10.58K,11.52K,30.88K, 1680,49.88K,7620,11.96K,11.34K,24.23K, 2760,55.24K,10.5K,12.6K,12.14K,17.76K, 4960,64.76K,13.84K,12.44K,12.54K,6785, 6040,70.28K,15.6K,13.26K,12.5K,0, 0,0,0,240,1970,69.19K, 0,400,0,1160,3660,65.18K, 0,1920,100,3200,5225,63.34K, 0,11.36K,840,5960,7310,56.03K, 280,21.48K,2260,7940,7645,48.06K, 320,24.24K,3560,7700,8310,45.69K, 520,27.28K,4220,7880,8515,41.51K, 840,30.2K,4560,9000,8105,40.02K, 1560,36.6K,6340,9500,8670,34.79K, 2720,41.44K,8020,8980,8765,28.74K, 3800,46.8K,11.32K,9320,9065,21.54K, 4840,51.56K,13K,9920,8965,15.86K, 0,40,0,240,1760,68.91K, 0,400,0,1000,3250,66.07K, 0,2000,40,2840,4960,64.21K, 40,10.76K,620,5840,6530,57.9K, 240,19.08K,2700,7140,7465,51.14K, 440,21.88K,2640,7200,7745,47.26K, 480,24.32K,3400,7280,7825,45.41K, 800,27.16K,4840,7720,7635,42.95K, 1480,31.48K,6400,8240,8005,38.58K, 1560,35.24K,8260,8180,8140,33.54K, 3520,41.72K,10.2K,8480,8150,25.43K, 4800,44.64K,12.72K,8880,8450,21.6K, 0,0,0,200,1175,69.54K, 0,440,0,880,2350,67.43K, 0,1880,40,1900,3170,66.83K, 80,7520,520,3840,4335,60.15K, 240,13.48K,1580,4800,4700,55.46K, 240,15.64K,2100,5300,4540,54K, 320,17.4K,2460,4780,4900,53.44K, 600,18.72K,3280,5180,5320,50.47K, 1120,22.56K,4680,5020,5215,47.86K, 1720,25.36K,5920,5400,5465,44.45K, 2800,28.64K,7880,5360,5300,38.8K, 3680,29.64K,8780,5500,5460,35.68K, 0,0,0,0,385,69.61K, 0,80,20,160,605,69.9K, 0,320,40,500,850,68.25K, 0,2040,280,940,1420,67.09K, 0,2800,620,1400,1510,66.07K, 240,3360,780,1280,1505,65.06K, 320,3720,980,1340,1650,64.08K, 160,4200,1200,1360,1640,64.99K, 400,4720,1540,1380,1930,63.53K, 760,4880,2100,1620,1660,62.5K, 1040,5560,2680,1740,1725,61.12K, 1240,6160,2900,1900,1855,60.8K, 0,0,0,0,165,68.36K, 0,40,0,100,355,69.85K, 0,80,0,360,515,69.19K, 0,880,140,620,785,68.76K, 40,1560,380,740,835,67.57K, 40,1680,540,760,950,66.84K, 40,1800,540,800,930,65.77K, 120,1960,700,580,1010,67.28K, 200,2000,940,740,1100,66.83K, 440,2640,1060,880,1030,64.92K, 680,2600,1560,1020,1015,65.04K, 1040,2600,1600,980,1195,64.75K, 0,0,0,0,40,69.97K, 0,0,0,0,165,70.24K, 0,0,0,40,275,69.01K, 0,80,120,160,405,69.13K, 80,280,120,300,435,69.31K, 0,200,200,340,565,68.25K, 80,360,280,280,475,67.86K, 120,280,380,300,575,67.71K, 80,440,440,280,465,68K, 240,440,440,380,555,68.45K, 320,440,540,480,615,67.87K, 640,440,560,420,600,67.89K, 0,0,0,0,0,66.35K, 0,0,0,180,2480,65.16K, 0,480,0,1500,4830,62.14K, 0,2440,80,3960,6780,59.88K, 40,14K,1060,7700,9590,49.94K, 200,27.48K,2900,9920,10.88K,40.53K, 240,30.92K,3600,10.42K,10.86K,36.33K, 720,34.64K,4580,10.8K,11.26K,32.82K, 920,38.84K,5100,10.88K,11.15K,30.45K, 1480,46.48K,6900,12.08K,11.57K,23.62K, 2200,52.36K,9400,12.02K,12.26K,16.72K, 3760,60.92K,12K,13.46K,12.09K,6920, 5080,68K,15.14K,13.42K,12.27K,0, 0,0,0,220,1895,65.67K, 0,320,0,1140,3345,64.9K, 0,1800,0,2680,5305,61.22K, 0,10.48K,840,5980,6975,52.15K, 320,20.48K,2300,7280,7645,45.65K, 360,23K,3040,8100,8295,43.47K, 440,26.08K,3460,8180,7970,40.47K, 800,28.68K,4560,8780,8410,38.52K, 1480,35.44K,6000,9080,8830,33.02K, 1880,39.12K,7760,8480,8830,28.42K, 3240,45.04K,10.04K,9380,8895,20.34K, 4080,50.12K,12.28K,9440,9055,15.15K, 0,0,0,180,1750,64.88K, 0,360,0,940,3240,64.07K, 0,1840,80,2820,4545,60.91K, 0,9360,680,5560,6645,55.02K, 320,18.72K,2060,6840,7195,47.27K, 240,20.68K,2300,7220,7475,45.15K, 680,23.04K,3240,7800,7300,43.32K, 600,26.12K,4660,7520,7860,40.92K, 1040,30.36K,5860,7820,7665,36.22K, 1960,33.56K,7000,8280,7735,31.75K, 3600,38.84K,10.1K,8620,8335,24.59K, 4320,43.16K,11.4K,8960,8285,20.02K, 0,0,0,140,1200,64.99K, 0,320,0,760,2230,63.75K, 0,1320,60,1760,3520,63.3K, 40,6520,420,4160,4395,59.49K, 240,12.92K,1700,4440,4770,53.81K, 80,15.16K,1900,4900,4570,51.84K, 400,16.88K,2320,4520,4780,50.08K, 640,17.92K,2920,5260,5055,48.43K, 840,21.48K,4400,5260,4865,45.41K, 1600,24.36K,5240,5800,5225,42.27K, 2480,27.36K,7360,5700,5120,37.83K, 3840,29.6K,7920,5680,5395,34.21K, 0,0,0,40,315,66.29K, 0,40,0,160,630,65.97K, 0,320,40,300,835,64.71K, 0,1680,180,940,1450,64.42K, 120,2840,500,1380,1465,63.36K, 40,3320,920,1100,1555,61.93K, 120,3560,920,1460,1540,62.08K, 280,3800,1080,1540,1615,61.77K, 280,4440,1420,1540,1675,61.06K, 480,4680,1980,1440,1785,59.64K, 760,5480,2740,1460,1705,56.66K, 880,6040,2920,1620,1790,57.62K, 0,0,0,0,125,65.44K, 0,0,0,40,355,66.75K, 0,120,20,220,520,65.58K, 0,560,160,680,765,65.18K, 0,1360,380,700,1060,63.87K, 0,1400,500,680,1090,64.19K, 80,1840,440,800,1005,63.97K, 120,1840,660,640,1010,63.37K, 160,2000,920,900,1065,62.9K, 200,2480,1020,920,1145,63.33K, 480,2480,1580,900,1115,61.16K, 560,2480,1660,1080,1115,61.09K, 0,0,0,0,70,65.69K, 0,0,0,20,170,65.23K, 0,0,0,60,230,67.08K, 0,120,40,160,370,64.92K, 0,280,220,280,430,65.31K, 80,200,200,400,495,65.44K, 120,200,200,360,490,65.06K, 80,280,180,300,545,65.79K, 240,280,340,480,500,64.73K, 240,400,540,500,520,65.23K, 240,360,480,480,515,65.08K, 560,440,500,520,460,64.21K, 0,0,0,0,0,63.28K, 0,40,0,300,2195,62.53K, 0,240,0,1080,4785,58.99K, 0,2560,40,3640,6540,57.04K, 40,12.72K,780,7540,9570,47.47K, 80,25.64K,2680,9340,10.85K,37.51K, 360,29.32K,3280,10.12K,10.72K,34.91K, 600,33.28K,3460,9900,11.2K,31.6K, 720,37.2K,4520,10.5K,11.31K,28.18K, 1360,43.92K,6800,11.42K,11.69K,21.54K, 2120,49.88K,8340,12.1K,11.82K,15.23K, 3480,59K,11.64K,12.58K,12.28K,6405, 4840,65.44K,13.18K,13.5K,12.26K,0, 0,40,0,180,1870,61.52K, 0,240,20,1040,3255,60.69K, 0,1720,80,2740,5100,58.1K, 0,10.04K,600,6020,7030,50.82K, 240,19.48K,2000,6900,7955,43.84K, 320,22.32K,2180,7620,7910,40.75K, 560,24.84K,3140,8020,8510,38.77K, 600,27.84K,3700,7940,8530,36.35K, 1160,33.12K,5560,8240,8700,31.43K, 1840,37.92K,7120,8500,8550,26.88K, 2520,43.88K,9460,9440,9160,19.36K, 4840,47.32K,11.06K,9660,9015,14.85K, 0,0,0,120,1680,62.25K, 0,240,0,1000,2990,61.28K, 0,1560,60,2660,4470,58.3K, 120,8880,660,5280,6325,52.76K, 360,17.32K,1680,6800,7260,45.99K, 560,19.48K,2340,7020,7420,43.07K, 480,21.84K,3080,7040,7590,41.09K, 680,24.24K,3680,7340,7935,39.96K, 1280,29.28K,5060,7740,7710,35.07K, 1360,32.16K,7080,8480,8090,29.73K, 3200,37.76K,8700,8800,8025,23.52K, 3360,42.16K,10.16K,8960,8250,19.35K, 0,0,0,160,1010,62.27K, 0,200,0,620,2245,61.95K, 0,1400,0,1580,3210,59.82K, 40,5720,380,3940,4270,55.42K, 160,11.6K,1300,4360,4585,50.5K, 280,13.56K,1880,5360,4665,49.21K, 240,15.36K,2240,4860,4950,47.74K, 680,17.24K,2680,4860,4915,46.97K, 840,20.16K,3700,5020,5220,42.76K, 1200,23.76K,4500,4960,5360,39.74K, 2280,26.56K,7000,5320,5210,35.46K, 3040,28.68K,7480,5920,5210,32.69K, 0,0,0,0,220,62.58K, 0,0,0,180,665,62.7K, 0,40,0,480,835,61.8K, 0,1520,280,1040,1240,61.36K, 40,3000,600,1180,1535,60.61K, 160,3200,760,1480,1600,58.95K, 40,3360,940,1400,1680,59.26K, 80,3400,960,1560,1660,59.17K, 320,4480,1320,1480,1715,57.46K, 240,4880,1900,1500,1710,57.32K, 520,5440,2420,1620,1685,55.15K, 1080,5720,2720,1660,1790,54.31K, 0,0,0,0,170,63.06K, 0,0,0,80,375,63.55K, 0,80,20,220,500,62.61K, 0,680,80,580,720,61.75K, 40,1280,440,640,1015,60.91K, 80,1360,340,740,895,60.68K, 40,1720,600,800,1020,61.62K, 80,1760,580,800,945,61.44K, 280,2080,720,940,925,60.58K, 200,2400,1120,900,1090,59.12K, 440,2360,1540,1040,1060,58.39K, 680,2840,1440,900,1035,56.67K, 0,0,0,0,85,63.35K, 0,0,0,20,105,63.44K, 0,0,0,20,200,62.29K, 0,40,40,240,365,63.52K, 0,240,180,240,460,61.37K, 120,240,160,340,380,62.48K, 0,280,360,280,460,62.02K, 40,200,320,380,380,62K, 160,480,320,360,490,61.88K, 160,280,420,460,490,62.26K, 240,560,440,540,415,61.46K, 400,360,620,520,525,61.4K, 0,0,0,0,0,59.24K, 0,0,0,160,2220,58.85K, 0,200,0,1520,4380,56.36K, 0,2240,80,3140,6740,53.82K, 80,12.28K,520,7380,9395,44.17K, 320,24.36K,1880,9520,10.76K,35.99K, 200,28.12K,2700,9660,11.02K,32.27K, 440,30.96K,3840,10.24K,11.02K,30.08K, 480,35.84K,4000,10.74K,11.35K,27.26K, 960,41.48K,6000,12.06K,11.68K,21.05K, 1480,48.48K,7220,11.6K,11.77K,14.59K, 3320,55.92K,10.62K,12.84K,12.21K,6010, 3760,63.68K,12.7K,12.32K,12.22K,0, 0,0,0,200,1665,58.92K, 0,440,0,820,3340,58.51K, 0,1560,60,2560,4990,54.33K, 0,9360,420,5340,7025,49.24K, 200,17.84K,1620,7360,7900,41.19K, 360,20.92K,2440,7260,7870,39.55K, 240,24.12K,3200,7420,8420,37.04K, 600,26.32K,3320,8040,8170,34.05K, 1040,31.6K,4600,8860,8420,29.59K, 1280,36.12K,6620,8480,8645,25.4K, 2440,42.08K,9060,9320,8605,18.26K, 3480,46.2K,10.42K,9060,8935,14.08K, 0,0,0,80,1625,58.92K, 0,320,0,680,3230,58.59K, 0,1520,20,2280,4555,55.08K, 40,8040,480,5220,6385,49.2K, 160,16.12K,1720,6600,7300,44.12K, 200,17.56K,2180,6280,7645,40.9K, 360,21.08K,2800,6740,7305,39.31K, 520,23.76K,3500,6560,7625,36.92K, 800,27.28K,5280,7860,7790,33.21K, 1120,31.44K,6520,7960,7930,28.98K, 1960,37.44K,8840,8140,8185,21.9K, 3240,40.64K,9820,8680,8105,18.63K, 0,40,0,180,1050,59.05K, 0,240,20,520,1970,58.49K, 0,1160,40,1520,3125,57.1K, 0,5960,340,3340,4100,53.13K, 240,11.32K,1000,4480,4550,47.86K, 160,13.32K,1560,4920,4780,45.38K, 320,14.44K,2180,5120,4605,45.04K, 400,16.6K,2360,4980,5070,44.11K, 800,19.32K,3380,5080,4850,40.92K, 1160,21.76K,4600,5740,5015,37.93K, 1680,25.16K,5980,5900,5340,34.4K, 2440,27.4K,7280,5780,5385,31.46K, 0,0,0,20,240,59.64K, 0,0,0,160,555,59.42K, 0,200,0,400,955,59.6K, 0,1160,60,960,1440,59K, 0,3040,320,1120,1470,56.26K, 40,2840,560,1620,1450,56.31K, 120,3080,660,1260,1665,55.96K, 120,3280,920,1480,1740,55.11K, 280,4360,1400,1500,1665,53.45K, 320,4600,1520,1580,1880,54.07K, 680,5200,2360,1480,1790,53.23K, 1160,5760,2280,1860,1760,51.44K, 0,0,0,0,185,59.9K, 0,0,0,100,355,59.01K, 0,40,20,240,490,59.53K, 0,720,160,580,775,58.91K, 40,1240,340,660,915,56.28K, 80,1200,380,660,1035,57.88K, 80,1320,460,800,995,57.19K, 40,1640,480,660,1040,56.81K, 120,2080,780,820,1075,57.79K, 320,2160,860,820,1115,56.12K, 240,2360,1500,800,1085,56.05K, 440,2680,1540,1180,1125,54.82K, 0,0,0,0,55,59.34K, 0,0,0,0,100,59.54K, 0,0,0,0,225,59.08K, 0,40,80,180,355,59.99K, 0,200,120,280,405,59.07K, 40,160,180,420,345,59.51K, 120,160,160,340,435,59.26K, 80,160,200,400,530,58.68K, 120,480,320,400,480,59.25K, 240,360,480,380,450,58.54K, 240,360,500,460,590,58.51K, 440,480,520,380,510,58.9K, 0,0,0,0,0,56.96K, 0,0,0,120,1840,55.66K, 0,240,0,1160,4275,53.11K, 0,1720,20,3380,6420,51.02K, 40,11.32K,580,7100,9250,42.84K, 80,21.92K,1980,8960,10.86K,34.07K, 200,26.4K,2700,9460,10.61K,30.68K, 360,28.24K,3220,10.92K,10.51K,27.79K, 400,32.84K,3880,10.78K,11.3K,25.49K, 920,40.12K,4960,10.4K,11.61K,19.29K, 1200,46.6K,7220,11.2K,11.38K,14.03K, 2560,54.16K,9580,11.98K,11.97K,5585, 3120,59.88K,12.1K,12.16K,12.15K,0, 0,0,0,160,1720,54.65K, 0,280,0,860,3395,53.97K, 0,1480,20,2160,4815,51.69K, 80,8680,540,5380,6930,46.06K, 240,17.2K,1360,6600,7510,38.55K, 320,18.84K,2020,7280,7840,37.38K, 400,22.44K,2440,7200,8340,34.39K, 480,24.68K,3100,8220,8360,31.96K, 640,28.76K,4300,8720,8400,28.49K, 1160,34.72K,5400,8380,8495,24.04K, 2200,39.96K,7880,9520,8600,17.27K, 3280,44.96K,9420,9240,8750,13.5K, 0,0,0,60,1580,56.8K, 0,120,0,1000,2800,54.73K, 0,1440,20,2400,4260,52.42K, 40,8000,360,4640,6385,47.23K, 40,14.96K,1620,6620,6910,41.37K, 80,16.4K,2180,7040,7100,38.32K, 360,20.16K,2640,6640,7205,36.33K, 240,22.24K,3000,7140,7660,34.65K, 880,26.36K,4020,7040,7795,30.42K, 1720,30.36K,5080,7760,7880,27.06K, 1920,34.88K,8020,8180,8445,21.19K, 2720,39K,8740,8160,8180,17.37K, 0,0,0,120,960,55.41K, 0,80,0,580,2095,54.83K, 0,1000,60,1440,3090,53.67K, 0,5280,460,3500,4030,50.24K, 160,10.64K,1080,4600,4600,46.31K, 80,12.2K,1380,4640,4880,44.36K, 360,13.84K,1600,4940,4595,41.94K, 440,15.96K,2000,4940,4980,41.6K, 680,18.72K,3100,5140,4985,38.67K, 840,20.92K,3780,5380,5165,35.98K, 1880,24.32K,5340,5720,5220,32.03K, 2120,26.52K,7020,5940,5175,29.96K, 0,0,0,20,300,57.17K, 0,80,0,60,575,56.51K, 0,240,20,420,895,55.92K, 0,1280,240,1000,1250,54.59K, 0,2680,440,1060,1515,53.91K, 0,2840,620,1240,1415,53.21K, 0,2920,660,1340,1530,52.51K, 120,3240,740,1480,1425,52.49K, 40,3680,1360,1680,1670,52.06K, 280,4880,1620,1440,1590,51.1K, 440,4920,2240,1660,1805,49.31K, 840,5520,2300,1620,1690,48.89K, 0,0,0,0,125,55.64K, 0,0,0,60,355,56.13K, 0,80,0,140,585,55.63K, 0,520,60,540,690,56.3K, 40,1160,200,560,965,54.73K, 80,1080,340,880,940,55.52K, 80,1320,480,700,940,54.66K, 80,1680,400,760,960,54.57K, 80,1800,580,820,1080,53.83K, 280,2000,860,920,1050,53.76K, 440,2160,900,920,1070,52.42K, 560,2560,1280,940,1100,51.5K, 0,0,0,0,60,56.59K, 0,0,20,0,110,56.59K, 0,0,0,100,225,57.46K, 0,40,40,220,375,56.82K, 0,160,140,200,510,56.11K, 0,200,180,320,445,56.11K, 40,160,120,320,425,56.08K, 120,200,200,380,440,55.87K, 80,320,280,360,525,56.01K, 200,360,280,480,475,54.56K, 120,440,520,380,485,54.77K, 480,320,440,540,470,54.42K, 0,0,0,0,0,53.04K, 0,0,0,100,1790,51.84K, 0,280,0,1180,3935,50.18K, 0,1880,0,2900,6170,46.83K, 0,10.68K,540,6440,9035,39.33K, 40,19.88K,1480,9140,10.36K,32.06K, 240,24.36K,2180,9180,10.52K,28.77K, 360,27.08K,2280,9780,10.98K,26.22K, 480,30.68K,3160,10.1K,10.51K,23.65K, 720,37.72K,4560,11.2K,11.27K,18.45K, 1040,42.44K,6560,11.44K,11.46K,12.89K, 2120,51.52K,8300,11.74K,11.72K,5150, 2760,56.76K,10.46K,12.72K,12.09K,0, 0,0,0,100,1790,51.24K, 0,240,0,780,3195,51.27K, 0,1320,40,1820,4860,48.86K, 0,7480,400,5160,6985,42.52K, 120,15.52K,1320,6820,7540,36.39K, 80,17.96K,1540,7000,7635,34.58K, 440,20K,2240,7600,7735,32.64K, 520,22.92K,2880,8000,8125,29.85K, 600,27.56K,3800,7940,8490,27.29K, 840,32.92K,4960,8100,8460,22.48K, 1520,38.12K,6920,9080,8610,16.17K, 2680,41.52K,9020,8900,9005,11.59K, 0,0,0,120,1350,51.09K, 0,120,0,740,2720,51.33K, 0,1120,20,1980,4350,48.86K, 80,7400,260,4160,6425,44.17K, 40,13.64K,1160,6480,6790,38.79K, 200,15.6K,1620,6560,7105,35.59K, 360,18.4K,2340,6640,7020,35.06K, 400,20.44K,2580,7000,7710,32.23K, 680,24.28K,3740,7600,7535,29.16K, 920,27.88K,4640,8260,7625,25.24K, 1600,34.44K,6660,8040,7770,20.54K, 2200,37.28K,7920,8460,7795,15.92K, 0,0,0,60,930,53.19K, 0,160,0,580,2110,51.06K, 0,840,0,1140,3115,49.79K, 40,4720,240,3800,3950,45.82K, 40,9760,840,4720,4595,43.18K, 40,11.64K,1340,4100,4745,41.48K, 120,13.04K,1440,4880,4830,40.02K, 200,14.52K,1920,4740,4930,38.3K, 440,17.44K,2460,4880,5135,36.38K, 880,20.12K,3540,5380,5225,33.9K, 1400,23.32K,5040,5180,5210,30.11K, 1920,25.08K,5780,5660,5370,27.4K, 0,0,0,0,355,52.3K, 0,0,0,120,520,51.77K, 0,80,0,420,950,51.53K, 0,960,80,1060,1370,51.86K, 40,2360,320,1140,1425,50.35K, 40,2560,420,1280,1360,49.39K, 120,2920,460,1200,1625,49.9K, 0,3000,700,1360,1600,48.85K, 200,3720,1240,1360,1665,49.02K, 360,4360,1460,1460,1560,47.98K, 360,4720,1780,1620,1730,46.13K, 560,5160,1740,1640,1660,45.51K, 0,0,0,0,95,52.53K, 0,0,0,60,280,53K, 0,40,20,160,450,53.51K, 0,400,20,540,780,51.26K, 0,920,140,560,890,51.19K, 80,1320,240,620,990,51.82K, 40,1360,260,780,945,50.22K, 40,1720,460,720,900,50.58K, 40,1640,640,780,985,50.72K, 240,1960,1060,720,1055,50.43K, 200,2280,1140,820,990,49.11K, 400,2400,1280,820,1070,48.07K, 0,0,0,0,65,52.32K, 0,0,0,40,80,52.72K, 0,0,0,40,195,52.84K, 0,80,40,160,320,52.11K, 0,240,80,280,415,51.49K, 40,240,100,220,425,52.17K, 40,200,220,300,445,51.91K, 40,120,240,400,430,52.24K, 0,320,300,440,455,51.89K, 80,360,380,500,465,52.32K, 240,400,460,420,510,50.97K, 120,360,580,360,470,51.04K, 0,0,0,0,0,48.36K, 0,0,0,80,1865,47.02K, 0,160,0,820,3815,46.58K, 0,1240,40,2560,6005,42.95K, 0,9200,520,6080,8385,36.65K, 80,18.64K,1360,8060,9605,28.37K, 80,21.64K,1860,9160,10.31K,26.65K, 120,25.12K,2300,9460,10.57K,24.16K, 320,27.04K,2960,10.06K,11.09K,21.93K, 480,33.92K,4020,11.08K,11.29K,17.17K, 840,40.08K,5160,10.86K,11.25K,11.83K, 1520,46.44K,6920,12.08K,12.34K,4860, 2160,52.48K,9680,12.02K,11.98K,0, 0,0,0,160,1455,48.12K, 0,120,0,640,2990,46.2K, 0,1040,20,2020,4345,44.13K, 0,6640,280,4700,6620,39.7K, 40,14.24K,1220,6200,7560,32.73K, 160,16.4K,1320,6780,7405,32.01K, 120,19.04K,1860,6840,7915,29.78K, 320,20.92K,2160,7280,8015,27.56K, 440,25.4K,3340,8000,8165,24.24K, 800,29.4K,4080,8460,8545,20.61K, 1320,36.88K,5860,8860,8315,14.77K, 1640,38.88K,8060,8460,8910,11.47K, 0,0,0,120,1280,48.71K, 0,160,0,540,2770,46.81K, 0,800,20,2180,4105,45.16K, 0,6120,400,4240,6010,40.29K, 80,12.64K,1240,6140,6815,35.94K, 80,14.84K,1280,6260,7065,33.64K, 240,16.44K,1780,6640,7065,32.39K, 160,18.8K,2140,6620,7485,30.34K, 240,22.6K,2940,7140,7780,26.43K, 600,26.16K,4100,7400,7630,23K, 1320,31.64K,5880,8340,7455,18.32K, 1680,33.8K,7300,8460,8065,14.65K, 0,0,0,40,815,48.58K, 0,160,0,400,1875,47K, 0,720,20,1120,2930,45.54K, 40,4320,280,3220,3975,42.81K, 80,8560,880,4380,4365,38.65K, 80,9960,860,4320,4630,38.55K, 0,12.28K,1200,4280,4820,37.11K, 240,13.12K,1700,4700,4820,35.58K, 400,15.88K,2020,5280,4765,34.13K, 440,18.8K,3260,5060,5170,32.25K, 1080,21.68K,4060,5880,5120,28.06K, 1080,23.64K,5420,5160,5485,25.17K, 0,0,0,20,310,48.67K, 0,0,0,80,490,48.17K, 0,120,20,380,825,47.79K, 0,1080,100,660,1355,46.51K, 0,2000,340,1020,1480,46.51K, 0,2560,260,1140,1350,45.87K, 80,2760,480,1280,1455,45.3K, 40,3040,400,1340,1485,44.76K, 120,3280,880,1460,1665,44.32K, 160,4080,1100,1500,1645,43.24K, 360,4680,1660,1660,1560,42.91K, 240,5240,1840,1800,1570,42.22K, 0,0,0,0,175,48.84K, 0,0,0,60,215,48.82K, 0,0,20,200,380,48.54K, 0,520,60,420,740,47.63K, 0,760,120,660,810,47.29K, 0,1280,220,580,900,46.72K, 40,1040,260,920,945,45.81K, 0,1600,380,580,960,46.35K, 120,1640,300,700,965,46.25K, 80,2080,640,760,970,45.32K, 120,2160,920,820,1000,45.5K, 440,2160,1040,1080,1075,45.07K, 0,0,0,0,45,47.89K, 0,0,0,0,95,47.94K, 0,0,0,0,150,47.36K, 0,80,0,140,315,48.44K, 0,160,60,320,380,47.69K, 0,40,120,280,480,48.23K, 40,240,100,320,475,48.23K, 0,200,200,340,395,47.8K, 0,200,300,400,490,47.62K, 120,280,300,360,475,47.2K, 160,440,260,380,480,47.39K, 280,360,400,340,470,46.99K, 0,0,0,0,0,44.03K, 0,40,0,20,1905,43.86K, 0,80,0,820,3695,42.16K, 0,1120,20,2300,5495,39.65K, 40,7600,300,5580,8700,33.01K, 40,16.4K,940,8020,9665,25.66K, 80,19.76K,1520,8640,9830,24.07K, 200,22.52K,1300,9600,10.51K,22.46K, 120,24.64K,2480,9880,10.83K,20.27K, 200,31.56K,3200,9880,11.16K,14.59K, 600,36.56K,4220,10.82K,11.42K,10.93K, 1200,44.16K,5980,11.4K,11.15K,4300, 1600,48.6K,7700,11.58K,11.5K,0, 0,0,0,140,1400,43.43K, 0,160,20,560,2650,42.21K, 0,760,0,1900,4300,41.34K, 0,5160,220,4500,6520,36.28K, 40,12.72K,580,6640,7145,30.35K, 80,14.52K,960,6540,7445,28.82K, 240,16.48K,1580,6880,7675,27.86K, 120,18.52K,1860,7340,7975,24.83K, 280,22.68K,2720,7300,8210,21.79K, 480,28.12K,3320,8040,8135,18.33K, 1160,32.76K,4940,8420,8665,13.79K, 1560,35.72K,5860,8420,8775,10.48K, 0,0,0,80,1195,43.23K, 0,200,0,540,2215,42.51K, 0,960,20,1420,4030,40.82K, 40,5160,260,3720,5990,35.93K, 80,10.68K,1000,6100,6740,31.96K, 40,13.52K,1320,5780,6870,29.98K, 160,14.92K,1480,5740,7100,28.43K, 240,16.76K,1840,6360,7030,27.91K, 240,20.76K,2540,6960,7380,24.63K, 760,23.52K,2980,7820,7505,21.07K, 720,28.64K,4820,7700,7835,17.01K, 1120,31.24K,6240,7940,7890,13.81K, 0,0,0,60,830,43.38K, 0,80,0,260,1765,43.33K, 0,600,0,1100,2615,41.46K, 0,3960,80,2960,3895,38.95K, 40,7720,660,4080,4330,35.26K, 40,8840,780,4580,4490,34.45K, 160,10.4K,960,4380,4405,33.74K, 120,11.8K,1620,4620,4670,32.85K, 240,14.04K,1900,4880,4985,29.98K, 320,16.84K,2420,4780,5130,28.06K, 600,20.2K,3500,5600,4940,25.29K, 1360,22.44K,4340,5540,5050,23.19K, 0,0,0,0,290,43.63K, 0,0,0,40,510,43.81K, 0,80,0,240,840,43.48K, 0,840,120,760,1185,43.25K, 0,1840,120,980,1345,41.9K, 0,2160,240,1040,1320,40.35K, 0,2280,580,1260,1440,41K, 0,2640,500,1160,1510,40.65K, 40,2880,660,1320,1560,40.26K, 160,3800,1140,1340,1640,39.82K, 240,4320,1220,1620,1650,38.48K, 240,4560,1700,1740,1565,37.85K, 0,0,0,0,160,44.57K, 0,0,0,20,270,43.86K, 0,0,20,100,460,43.45K, 0,480,20,400,635,43.79K, 40,880,200,700,760,42.64K, 0,1160,200,600,800,42.42K, 0,1120,200,600,880,42.89K, 40,1160,320,800,955,42.78K, 0,1720,340,660,960,41.58K, 40,1880,480,880,925,41.27K, 280,2000,820,840,1035,40.47K, 160,2080,800,960,965,40.33K, 0,0,0,0,40,43.88K, 0,0,0,0,75,44.21K, 0,0,0,0,160,43.99K, 0,0,0,180,310,43.08K, 0,160,40,120,435,43.33K, 0,80,80,280,440,43.43K, 0,240,140,260,455,42.44K, 0,200,180,320,480,43.9K, 80,200,180,300,465,43.21K, 40,360,300,300,440,42.66K, 120,280,300,360,510,42.05K, 200,320,440,360,555,42.79K, 0,0,0,0,0,39.35K, 0,0,0,20,1535,37.95K, 0,80,0,460,3375,37.6K, 0,1000,20,1840,5380,35.16K, 0,7040,160,5140,8050,30.27K, 40,14.48K,560,7480,9630,23.9K, 40,16.84K,1000,7800,9840,21.86K, 120,18.92K,1320,8400,10.28K,19.71K, 160,21.4K,1780,9020,10.53K,17.83K, 400,27.56K,2400,10.1K,10.78K,13.56K, 360,31.76K,3540,10.62K,10.94K,10.15K, 840,38.64K,4820,11.6K,11.7K,3775, 1160,44.64K,6180,11.06K,11.61K,0, 0,0,0,40,1260,38.97K, 0,120,0,440,2485,38.5K, 0,600,0,1500,4175,35.69K, 0,4760,180,4460,6000,31.58K, 80,11.48K,340,5740,6875,27.82K, 40,12.76K,960,6000,7340,25.76K, 80,14.72K,1260,6520,7140,23.43K, 40,17.4K,1100,6940,7525,22.62K, 280,20.4K,1780,7040,8205,20.57K, 400,26.04K,2680,7560,7965,16.73K, 680,29.84K,3900,8140,7995,11.88K, 760,32.96K,4780,8300,8090,9200, 0,0,0,120,1055,38.74K, 0,80,0,560,2210,37.95K, 0,720,20,1500,3425,36.34K, 0,4560,160,3860,5420,32.15K, 80,9240,640,5440,6595,28.33K, 0,11.28K,820,5420,6745,27.1K, 80,12.84K,1000,6140,6875,25.66K, 120,15.04K,1380,6040,6970,24.62K, 280,17.52K,2020,6860,7310,22K, 440,21.36K,2200,7460,7400,18.67K, 720,26.08K,4100,7460,7670,14.34K, 1240,28.76K,4700,7800,7675,11.54K, 0,0,0,60,755,39.16K, 0,120,0,280,1610,38.21K, 0,520,20,780,2550,37.73K, 40,2920,180,3240,3680,34.05K, 40,6600,400,3980,4485,32.21K, 40,7640,500,4240,4345,30.76K, 80,8600,600,4540,4275,29.73K, 40,10.8K,920,4260,4380,29.45K, 80,13K,1340,4760,4765,26.74K, 320,15.28K,1760,4640,5130,25.39K, 560,18.12K,2600,5260,5010,22.44K, 960,20.68K,3580,5200,5185,19.97K, 0,0,0,40,245,39.33K, 0,0,0,60,465,38.35K, 0,40,0,280,685,39.41K, 0,680,0,780,1120,38.04K, 0,1600,120,1060,1360,36.99K, 40,1560,160,1100,1250,36.58K, 0,2200,200,1160,1385,36.56K, 0,2200,320,1200,1545,35.52K, 40,2840,480,1120,1430,35.81K, 120,3360,840,1400,1405,35.1K, 80,3960,1160,1460,1680,34.86K, 240,3880,1220,1560,1765,34.25K, 0,0,0,0,70,39.32K, 0,0,0,80,235,39.35K, 0,0,0,160,395,38.45K, 0,360,0,380,655,38.26K, 0,920,120,540,765,37.78K, 0,920,120,520,870,38.3K, 0,1000,140,560,975,37.51K, 40,960,220,760,840,37.72K, 40,1440,300,620,990,37.4K, 40,1480,440,760,1070,37.72K, 160,1840,680,880,995,36.46K, 160,2120,820,880,995,36.32K, 0,0,0,0,15,39.42K, 0,0,0,0,110,39.5K, 0,0,0,20,140,39.73K, 0,40,40,120,265,39.67K, 40,80,20,220,425,39.55K, 0,80,40,220,390,39.58K, 0,120,120,220,450,38.51K, 0,200,160,260,425,38.4K, 0,240,200,280,425,38.79K, 40,320,160,320,380,39.15K, 40,240,320,360,550,38.86K, 160,360,180,400,450,37.73K, 0,0,0,0,0,35.17K, 0,0,0,40,1375,33.97K, 0,0,0,480,2985,33.52K, 0,600,0,1740,4995,31.08K, 0,5040,120,4840,7895,26K, 40,12.32K,520,6880,9500,21.14K, 40,14K,800,7440,9280,19.32K, 200,16.16K,920,7960,9850,17.15K, 80,18.08K,1140,8280,10.04K,15.52K, 80,23.64K,1900,9640,10.35K,12.15K, 360,27.96K,2820,9880,10.63K,9100, 640,35.16K,4160,11.16K,10.71K,3310, 800,39.6K,4720,10.86K,11.49K,0, 0,0,0,60,1310,35.32K, 0,80,0,240,2370,33.92K, 0,640,0,1220,3910,32.09K, 0,3720,20,3780,6125,28.43K, 40,9040,520,5640,6930,24.36K, 0,10.16K,760,5820,7420,23.01K, 80,12.68K,820,6020,7580,21.17K, 120,14.16K,1080,7060,7280,20.17K, 80,18K,1460,6780,8075,17.58K, 280,22.04K,1960,7180,8115,14.91K, 400,26.52K,3560,7440,8330,10.52K, 680,29.12K,3980,7960,8600,7940, 0,0,0,20,980,34.17K, 0,0,0,340,2180,32.83K, 0,400,20,1420,3445,32.54K, 0,3640,200,3300,5335,29.02K, 0,7760,500,4800,6315,25.04K, 40,9520,520,5160,6340,24.37K, 80,12K,740,5320,6595,22.84K, 80,12.36K,1240,5820,6985,21.5K, 40,15.68K,1520,6260,7285,19.01K, 120,19.4K,2140,6760,7245,16.85K, 400,23.24K,2880,7400,7495,12.92K, 600,26.16K,3780,7360,7600,10.51K, 0,0,0,60,695,34.63K, 0,40,0,280,1610,34.16K, 0,480,20,680,2260,32.63K, 0,2480,140,2400,3525,30.87K, 40,5520,340,3840,4130,28.25K, 0,6440,340,3640,4575,27.08K, 80,7760,520,4200,4200,26.14K, 80,9160,680,4140,4560,25.89K, 240,10.6K,940,4980,4250,24.59K, 280,13K,1460,4880,4770,22.55K, 600,15.48K,2260,5160,5065,19.96K, 480,18.36K,2740,4860,5100,17.43K, 0,0,0,0,185,34.37K, 0,0,0,60,440,34.4K, 0,0,0,280,620,34.15K, 0,520,0,500,1170,34.17K, 0,1200,140,1080,1265,33.03K, 0,1400,120,880,1390,33.12K, 0,1800,200,1180,1360,32.83K, 0,1960,300,1080,1380,32.68K, 0,2520,420,1200,1535,31.88K, 40,2680,660,1300,1660,30.88K, 120,3560,620,1320,1635,30.69K, 160,3960,960,1520,1530,30.53K, 0,0,0,0,50,34.39K, 0,0,0,40,305,34.55K, 0,0,0,100,400,34.2K, 0,240,20,380,710,33.63K, 0,360,80,560,825,33.94K, 0,680,60,620,740,32.77K, 0,800,160,660,910,33.52K, 0,920,260,540,900,33.03K, 40,1040,140,620,935,33.68K, 0,1400,300,740,880,33.24K, 80,1720,420,860,930,32.26K, 80,1920,620,740,1080,32.24K, 0,0,0,0,35,34.43K, 0,0,0,0,80,35.33K, 0,0,0,0,160,35.11K, 0,0,20,120,300,33.8K, 0,40,60,240,385,34.62K, 0,80,80,200,335,33.66K, 0,160,40,140,400,34.97K, 0,120,80,200,465,35.12K, 0,160,140,220,470,34.08K, 80,240,140,240,390,34.49K, 40,240,200,340,520,34.44K, 40,280,180,360,485,33.25K, 0,0,0,0,0,30.61K, 0,0,0,20,1325,30.14K, 0,80,0,440,2795,29.51K, 0,680,20,1560,4505,27.08K, 0,3840,80,4160,7390,22.53K, 0,10.44K,460,6320,8825,18.22K, 40,11.24K,540,7200,8865,16.98K, 80,14K,560,7080,9430,15.03K, 40,16.28K,700,7960,9680,13.37K, 0,20.6K,1540,8120,10.52K,10.92K, 160,24.12K,1860,9880,10.59K,7310, 400,30.4K,2560,10.28K,10.89K,3030, 360,34.36K,3400,10.52K,11.07K,0, 0,0,0,20,1085,30.18K, 0,40,0,340,2105,30.22K, 0,400,0,980,3340,28.54K, 0,3160,120,3260,5640,25.02K, 0,7400,400,4560,6710,21.37K, 0,8520,560,5500,6760,19.84K, 80,10.16K,580,6100,7225,18.69K, 0,12.4K,740,5640,7575,17.81K, 120,16K,1240,6380,7525,15K, 120,18.92K,1640,7300,7410,12.77K, 320,22.04K,2260,8080,8355,9405, 480,26.12K,2480,7760,7780,7040, 0,0,0,0,875,30.09K, 0,40,0,340,1955,29.71K, 0,400,0,920,3075,28.43K, 0,2560,80,3520,4760,25.66K, 0,6880,260,4260,5995,22.33K, 40,7440,340,5100,6420,21.61K, 40,9960,420,4620,6705,19.78K, 80,10.2K,580,5700,6555,19.23K, 40,13.4K,1120,5860,6850,16.62K, 200,15.8K,1680,6800,7235,14.76K, 200,20.56K,2020,6620,7480,11.46K, 400,22.12K,2900,7300,7615,9760, 0,0,0,40,650,30.39K, 0,120,20,340,1395,29.66K, 0,160,0,600,2220,29.77K, 0,1680,100,2120,3390,26.97K, 0,4720,200,3580,3800,25.55K, 40,5880,240,3500,3970,24.38K, 40,5920,500,4160,4155,23.24K, 0,7520,520,3880,4540,22.27K, 0,9680,740,4300,4700,21.08K, 160,11.2K,1220,4700,4760,19.53K, 160,13.36K,1660,5060,4915,17.28K, 320,15.56K,2020,5240,4880,15.68K, 0,0,0,0,150,30.98K, 0,0,0,60,350,30.1K, 0,40,0,140,585,30.01K, 0,400,0,560,970,29.52K, 0,1120,0,680,1250,28.78K, 0,1040,240,1040,1250,29.26K, 0,1560,100,800,1390,28.36K, 0,1800,140,960,1330,28.96K, 0,2200,360,1120,1415,27.73K, 0,2600,580,1100,1485,27.72K, 0,3120,660,1280,1560,26.94K, 0,3400,720,1460,1575,26.62K, 0,0,0,0,55,30.84K, 0,0,0,40,260,30.64K, 0,0,0,80,360,31.01K, 0,80,40,360,600,30.59K, 0,480,80,340,660,30.07K, 0,640,100,500,725,29.25K, 40,680,240,600,795,29.87K, 0,680,160,560,780,29.75K, 0,1120,220,460,965,29.24K, 0,1240,360,560,975,29.09K, 40,1280,400,740,1055,28.19K, 80,1600,580,760,990,28.77K, 0,0,0,0,40,30.51K, 0,0,0,0,75,30.03K, 0,0,20,20,120,30.31K, 0,0,20,140,230,30.92K, 0,40,0,160,305,30.87K, 0,120,40,200,285,30.49K, 0,0,100,240,380,29.77K, 0,120,60,200,455,29.87K, 0,80,100,300,340,29.96K, 40,200,100,220,425,30.62K, 40,160,180,380,510,30.2K, 120,200,180,380,415,29.58K, 0,0,0,0,0,26.64K, 0,0,0,20,1140,26.48K, 0,40,0,400,2580,25.85K, 0,320,0,1320,4360,24.02K, 0,2600,60,3760,7030,20.65K, 0,8680,200,5880,8130,16.3K, 0,9160,380,6860,8625,14.67K, 0,11.2K,660,7180,8855,13.7K, 0,13.72K,420,7480,9070,12.25K, 40,16.8K,1120,7820,9635,9615, 80,20.48K,1300,9020,9895,6750, 160,24.72K,2220,10.22K,11.01K,2520, 280,29.2K,2800,9780,10.98K,0, 0,0,0,0,915,26.89K, 0,80,0,160,1950,25.54K, 0,320,0,780,3305,24.78K, 0,2240,100,2900,5385,21.89K, 0,5640,300,4800,6265,18.51K, 40,7640,280,4940,6630,18.17K, 40,8960,400,5120,6990,16.36K, 0,9880,440,5720,7150,15.73K, 120,13.08K,960,5920,7150,13.55K, 120,15.4K,1320,6780,7380,11.38K, 280,19.76K,1780,7040,7735,8500, 280,22.48K,2160,7180,7935,6140, 0,0,0,20,795,26.75K, 0,0,0,240,1725,25.91K, 0,120,20,1020,2990,24.68K, 0,1760,60,2960,4870,21.99K, 40,5480,260,4320,5705,19.43K, 0,6200,300,4460,5855,19.55K, 0,7560,480,5360,6075,17.3K, 80,9320,620,4720,6400,16.84K, 40,11.32K,760,5200,7095,15.15K, 120,13.92K,1260,6340,6690,12.7K, 80,17.76K,1740,6680,7235,10.32K, 160,19.48K,2340,6380,7300,8380, 0,0,0,20,640,26.85K, 0,0,0,200,1445,25.95K, 0,120,0,420,2220,26.15K, 0,1520,60,1860,3035,24.41K, 0,3840,280,3280,3795,22.11K, 40,4320,240,3600,4090,21.39K, 40,4720,240,3620,4105,20.99K, 40,6400,500,3540,4450,20.01K, 40,8000,520,4000,4690,18.74K, 120,9880,840,4260,4725,17.38K, 200,12.16K,1540,4540,4965,15K, 80,14.08K,1800,4560,4835,13.56K, 0,0,0,0,170,26.91K, 0,0,0,40,340,27.1K, 0,120,0,80,520,26.78K, 0,240,0,480,900,26.1K, 0,840,40,760,1060,25.24K, 0,920,120,940,1140,24.94K, 0,1120,60,940,1300,25.43K, 0,1360,120,860,1330,24.99K, 0,1920,80,1120,1380,25.07K, 0,2000,300,1240,1415,23.7K, 0,2840,500,1200,1650,24.02K, 40,2880,760,1400,1645,23.3K, 0,0,0,0,40,27.06K, 0,0,0,0,250,27.53K, 0,0,0,100,350,26.87K, 0,120,0,360,505,26.88K, 0,240,60,460,660,26.48K, 0,400,80,460,760,26.1K, 0,560,140,560,770,26.41K, 0,520,60,520,750,25.74K, 0,880,160,560,890,25.79K, 0,880,140,760,855,24.98K, 40,1160,300,680,850,24.77K, 120,1400,320,560,1055,24.94K, 0,0,0,0,35,26.72K, 0,0,0,0,40,27.59K, 0,0,0,0,110,27K, 0,40,0,20,270,26.37K, 0,40,0,120,340,26.78K, 0,0,20,180,340,26.52K, 0,0,80,260,355,26.62K, 0,80,120,180,400,26.94K, 0,80,40,280,340,26.05K, 0,200,160,220,400,26.14K, 0,280,180,280,445,26.48K, 80,120,120,360,530,26.45K, 0,0,0,0,0,23.98K, 0,0,0,60,1005,23.26K, 0,0,0,280,2200,22.6K, 0,160,20,720,4195,21.93K, 0,2600,20,3300,6455,18.52K, 0,6560,320,5520,8315,13.9K, 0,7560,320,5680,8545,13.47K, 40,9120,420,6320,8405,12.02K, 0,10.76K,420,6940,9190,10.8K, 0,13.8K,1020,7820,9520,8810, 40,17.56K,1040,8440,10K,5965, 120,22.52K,1800,9400,10.28K,2570, 200,25.24K,2280,9700,10.88K,0, 0,0,0,0,725,23.83K, 0,0,0,280,1735,23.32K, 0,200,0,600,3115,22.85K, 0,1560,120,2900,5095,19.24K, 40,4320,160,4360,6110,17.58K, 0,5880,360,4400,6285,15.94K, 40,7240,320,4860,6740,14.73K, 40,8720,400,5380,6705,14.04K, 0,10.68K,560,5640,6945,11.88K, 80,12.76K,640,6360,7055,10.24K, 120,16.48K,1520,6600,7935,7395, 200,19.04K,1640,7340,7965,5535, 0,0,0,0,775,24.12K, 0,0,0,160,1575,22.73K, 0,280,0,800,2605,22.39K, 0,1280,40,2300,4505,20.17K, 0,4520,160,3660,5570,16.41K, 40,4760,160,3940,5865,16.21K, 0,6480,280,4200,5800,15.34K, 40,7560,300,4580,6245,14.93K, 0,9400,660,5460,6215,13.38K, 40,11.36K,820,5400,6440,11.71K, 160,14.84K,1340,6440,6850,9130, 120,17K,1860,6240,7310,7485, 0,0,0,0,530,23.48K, 0,0,0,140,1190,23.01K, 0,80,0,360,1870,23.02K, 0,1040,80,1420,3055,21.05K, 0,2680,60,2900,3645,19.3K, 0,3560,160,2880,4090,19.15K, 0,4320,220,3120,4045,18.37K, 40,5040,320,3400,4300,17.83K, 0,6840,320,3920,4385,16.8K, 160,8320,620,4000,4620,15.53K, 160,10.56K,880,4440,4780,13.59K, 160,12.08K,1040,4720,4720,12.39K, 0,0,0,0,140,23.79K, 0,0,0,40,355,23.3K, 0,0,0,120,540,23.38K, 0,160,20,440,840,22.8K, 0,440,40,680,1185,22.7K, 0,920,20,820,1030,22.76K, 0,1200,60,700,1225,22.58K, 40,1080,160,860,1390,21.58K, 0,1680,120,940,1290,21.09K, 0,1640,260,1140,1420,21.77K, 40,2200,300,1240,1505,21.05K, 40,2440,540,1440,1510,21.28K, 0,0,0,0,65,23.8K, 0,0,0,20,195,23.58K, 0,0,0,60,270,24.24K, 0,160,20,220,525,23.94K, 0,280,20,300,675,22.84K, 0,400,20,500,660,23.39K, 0,440,40,600,690,22.18K, 0,600,40,580,760,22.86K, 40,680,100,480,825,22.61K, 0,880,140,600,725,22.38K, 0,1080,260,680,860,22.61K, 40,1160,340,820,820,21.68K, 0,0,0,0,30,23.79K, 0,0,0,0,40,24.16K, 0,0,0,0,110,23.62K, 0,0,0,80,240,24.02K, 0,0,0,160,310,24.05K, 0,0,40,200,280,23.76K, 0,40,80,60,375,23.89K, 0,0,40,240,355,23.61K, 0,80,40,200,375,23.32K, 0,120,20,200,430,23.47K, 120,160,80,320,355,23.09K, 0,240,140,260,485,23.23K, 0,0,0,0,0,21.56K, 0,0,0,0,900,21.56K, 0,0,0,140,1990,20.85K, 0,160,0,620,3485,19.13K, 0,1840,0,3080,6265,16.13K, 0,5360,240,5100,8110,12.77K, 0,5560,100,6000,7980,11.66K, 80,7720,180,5620,8600,10.68K, 0,9400,260,5940,8700,9595, 80,11.44K,620,7060,9525,7435, 40,14.72K,800,8040,9260,5400, 120,18.96K,1400,8680,9935,2115, 160,20.88K,1560,9380,10.45K,0, 0,0,0,0,590,21.57K, 0,40,0,140,1570,21.03K, 0,280,0,480,2615,19.95K, 0,1320,0,2320,4665,17.32K, 0,3640,100,3960,5825,14.68K, 0,4840,80,4280,5930,14.15K, 0,6320,180,4340,6590,13.55K, 0,7080,280,4540,6745,12.25K, 0,8360,480,5380,6775,10.52K, 120,10.32K,700,6460,7115,9300, 40,14.36K,1040,6480,7580,6675, 120,16.04K,1620,6880,7520,4985, 0,0,0,0,605,21.42K, 0,40,0,20,1405,20.37K, 0,40,0,460,2585,19.88K, 0,1160,0,2080,4105,17.97K, 0,3400,100,3160,5515,15.61K, 0,4200,80,3760,5515,14.75K, 0,5320,200,4080,5885,14.31K, 0,5840,300,4300,5980,13.4K, 0,7440,520,4680,6425,12.08K, 0,9760,740,4880,6620,10.55K, 120,13.04K,920,5780,6890,8035, 120,14.4K,1160,6080,7060,6580, 0,0,0,0,455,21.25K, 0,0,0,60,1010,21.13K, 0,200,0,320,1660,20.68K, 0,840,20,1420,3155,18.69K, 0,2200,160,2540,3485,17.19K, 0,3440,220,2680,3780,16.85K, 0,4040,120,2860,3970,15.95K, 0,4200,240,2900,4165,16.43K, 40,5720,200,3680,4175,14.48K, 40,6720,460,3760,4640,13.48K, 80,8760,980,4500,4365,12.03K, 120,10.44K,900,4540,4630,10.91K, 0,0,0,0,105,21.55K, 0,0,0,20,245,21.17K, 0,0,0,80,455,21.29K, 0,280,0,360,850,20.68K, 0,440,80,500,1150,20.74K, 0,600,0,820,1180,19.97K, 0,680,40,820,1215,20.61K, 0,960,80,820,1285,20.21K, 40,1280,180,1000,1260,19.74K, 0,1560,200,1060,1375,19.32K, 0,1960,300,1140,1365,19.12K, 0,2440,240,1300,1430,19.03K, 0,0,0,0,75,21.51K, 0,0,0,20,185,21.61K, 0,0,0,40,215,21.1K, 0,0,20,280,490,21.5K, 0,160,60,360,615,20.96K, 0,360,0,380,640,21.03K, 0,240,40,520,695,20.93K, 0,560,40,440,690,20.54K, 0,520,60,560,630,20.53K, 0,840,80,440,745,19.99K, 0,1040,220,700,820,20.22K, 80,1120,240,580,825,19.99K, 0,0,0,0,25,21.35K, 0,0,0,0,30,20.99K, 0,0,0,0,85,21.73K, 0,0,0,60,225,21.49K, 0,0,20,80,300,21.31K, 0,40,40,160,240,20.81K, 0,80,20,80,325,21.14K, 0,40,0,80,350,21.47K, 0,80,20,180,375,21.22K, 0,0,60,240,320,21.11K, 0,160,20,160,470,21.13K, 0,120,100,260,450,20.56K, 0,0,0,0,0,19.58K, 0,0,0,0,775,18.88K, 0,0,0,160,1915,18.6K, 0,120,0,740,3405,17.97K, 0,1520,0,2640,6005,14.44K, 0,4160,140,4480,7665,11.22K, 0,4840,220,5020,7805,11K, 0,5480,60,5900,8075,9950, 0,7840,240,5560,8520,8795, 0,9120,360,6680,8870,6915, 0,13.12K,560,7820,9535,4690, 40,16.28K,1020,8120,9685,1960, 80,18.76K,980,8940,9815,0, 0,0,0,0,475,19.23K, 0,0,0,80,1500,18.87K, 0,40,0,500,2470,18.29K, 0,1160,0,2300,4405,16.37K, 0,2880,40,3500,5650,13.81K, 0,4000,180,3660,5815,13.15K, 0,4680,100,4460,6355,12.22K, 40,5480,320,4440,6485,11.05K, 0,7440,500,4980,6675,9510, 40,9680,520,5780,6910,8455, 40,12.48K,700,6080,7355,6285, 80,13.64K,1220,6740,7560,4655, 0,0,0,0,575,19.33K, 0,0,0,140,1370,19.39K, 0,120,0,340,2220,18.36K, 0,1040,40,1880,4085,16.02K, 0,2960,40,2820,5130,13.87K, 0,3640,60,3360,5275,13.24K, 0,3880,100,3940,5585,12.53K, 0,5200,180,3780,5625,12.16K, 0,6240,420,4560,6110,10.79K, 0,8960,500,4980,6125,9535, 0,10.96K,1040,5520,6490,7210, 80,12.64K,820,6020,7175,6160, 0,0,0,40,415,19.09K, 0,0,0,120,955,18.95K, 0,80,0,200,1730,18.1K, 0,720,0,1340,2740,16.99K, 0,1480,20,2420,3525,15.88K, 0,2440,140,2460,3630,15.24K, 0,3200,120,2900,3695,14.93K, 0,3680,80,3040,3830,13.89K, 0,4800,200,3380,4055,13.59K, 0,5880,400,3620,4255,12.64K, 40,7720,780,4460,4245,11.41K, 120,9040,620,4380,4670,10.52K, 0,0,0,0,100,19.27K, 0,0,0,0,235,19.34K, 0,0,0,140,395,19.69K, 0,200,20,340,825,18.82K, 0,360,0,620,955,17.98K, 0,440,20,720,1130,18.06K, 0,680,20,680,1020,17.85K, 0,1000,40,760,1140,18.22K, 0,1000,160,920,1310,17.41K, 0,1240,60,1040,1325,17.14K, 0,2160,160,920,1305,17.1K, 0,2040,260,1240,1485,16.78K, 0,0,0,0,55,18.92K, 0,0,0,0,130,19.21K, 0,0,0,0,275,19.66K, 0,40,0,180,460,19.25K, 0,160,0,220,635,18.42K, 0,160,40,400,585,18.41K, 0,360,0,340,630,18.64K, 0,520,60,360,655,18.38K, 0,400,80,420,685,18.44K, 0,640,120,520,860,18.93K, 40,1000,140,580,785,18.15K, 40,760,280,520,880,18.08K, 0,0,0,0,20,19.56K, 0,0,0,0,60,18.85K, 0,0,0,0,85,19.01K, 0,0,0,60,170,19.28K, 0,0,0,80,295,19.19K, 0,0,20,80,290,19.61K, 0,40,60,60,320,19.1K, 0,0,20,100,315,19.2K, 0,80,40,180,270,18.95K, 0,80,80,220,340,18.87K, 40,120,100,80,425,18.87K, 0,120,100,280,420,18.69K, 0,0,0,0,0,18.45K, 0,0,0,0,690,17.57K, 0,0,0,80,1740,16.8K, 0,120,0,320,3245,15.89K, 0,1280,40,2420,5595,13.34K, 0,3160,140,4300,7085,10.97K, 0,4440,80,4380,7405,9995, 0,5240,220,4820,7950,8660, 0,6400,160,5720,8125,7910, 0,8320,300,6460,8760,6255, 80,11.24K,420,6700,8935,4735, 0,13.96K,680,7720,9560,1765, 80,16.28K,960,8340,9650,0, 0,0,0,0,535,17.48K, 0,0,0,140,1175,17.58K, 0,200,0,220,2235,16.64K, 0,720,20,1960,4365,14.39K, 0,2840,100,3080,5290,12.12K, 40,3440,120,3440,6035,11.63K, 40,4560,140,4040,5645,10.67K, 0,4280,140,4300,6180,10.48K, 0,6840,160,4280,6535,8860, 0,8520,440,5100,6555,7540, 0,10.92K,580,5480,7390,5510, 120,12.24K,980,6120,7295,4070, 0,0,0,20,525,17.71K, 0,0,0,120,1360,17.07K, 0,0,0,300,2195,16.71K, 0,760,20,1860,3835,14.82K, 0,2280,60,2900,5045,13.04K, 0,2560,140,3440,5130,12.06K, 0,3480,40,3540,5285,11.88K, 0,4520,220,3640,5565,10.85K, 0,5880,300,4180,5775,9990, 0,7480,420,4940,5980,8690, 0,9880,740,4720,6475,6635, 40,11.8K,900,5400,7005,5380, 0,0,0,0,415,17.72K, 0,0,0,60,890,17.58K, 0,120,0,160,1645,16.58K, 0,760,0,1000,2695,15.13K, 0,1560,100,1760,3350,14.4K, 0,2000,20,2400,3450,13.83K, 0,2600,120,2720,3435,14.18K, 0,3000,200,2760,3770,13K, 0,4320,240,2920,3860,11.97K, 0,4560,240,3700,4185,11.27K, 80,6560,480,4020,4445,10.35K, 40,8040,600,4180,4490,9045, 0,0,0,0,115,18.36K, 0,0,0,0,255,18.24K, 0,0,0,80,375,17.09K, 0,120,0,340,845,17.72K, 0,240,0,580,955,16.8K, 0,560,20,700,1065,17.21K, 0,520,60,720,1070,16.62K, 0,680,20,700,1275,16.47K, 0,760,20,980,1220,16.34K, 0,1360,40,640,1340,15.77K, 0,1680,200,1000,1305,15.42K, 40,2080,240,880,1430,15.87K, 0,0,0,0,60,17.5K, 0,0,0,0,90,17.25K, 0,0,0,40,230,17.79K, 0,40,0,160,400,17.62K, 0,160,20,200,660,16.77K, 0,80,0,400,580,16.73K, 0,480,0,260,680,17.02K, 0,400,40,380,600,17.22K, 0,400,60,380,690,16.39K, 0,600,80,460,715,17.43K, 0,800,160,560,850,16.4K, 0,640,140,640,745,16.78K, 0,0,0,0,15,17.99K, 0,0,0,0,65,18.5K, 0,0,0,0,90,17.67K, 0,0,0,0,190,17.54K, 0,40,20,60,245,17.49K, 0,0,20,140,250,17.38K, 0,0,0,100,260,17.9K, 0,40,40,80,255,17.65K, 0,0,40,140,355,17.14K, 0,120,100,160,360,17.57K, 0,0,40,120,350,17.33K, 0,120,100,200,440,16.9K, 0,0,0,0,0,17.05K, 0,0,0,0,635,16.21K, 0,0,0,160,1585,15.6K, 0,0,0,500,2750,15.29K, 0,880,40,2360,5455,12.23K, 0,2680,80,3680,6815,9945, 0,3560,80,4460,7260,9345, 0,4480,180,4580,7615,8165, 0,5280,300,5180,7755,7690, 0,7520,300,5720,8130,5500, 40,10.04K,260,6500,8695,4080, 40,12.4K,660,7240,9500,1760, 40,14.08K,900,8120,9495,0, 0,0,0,20,565,16.37K, 0,0,0,40,1275,15.55K, 0,0,0,340,2275,14.86K, 0,480,0,1760,4110,13.77K, 0,2040,60,2860,5265,11.06K, 0,3360,60,3140,5515,10.49K, 0,3480,40,3580,5910,10.44K, 0,3800,200,3800,5985,9840, 0,5360,320,4360,6485,8435, 40,6720,400,5200,6650,7145, 80,9400,520,5180,6870,5340, 80,10.96K,600,6100,7220,3960, 0,0,0,20,600,16.5K, 0,0,0,20,1195,16.51K, 0,80,0,320,1980,15.79K, 0,680,0,1560,3705,13.11K, 0,1600,20,2820,4710,11.7K, 0,2480,20,2920,5205,11.51K, 0,3120,120,3240,5245,10.81K, 0,3760,100,3660,5420,10.47K, 0,5600,200,4040,5575,9185, 0,6520,320,4580,5755,7585, 40,8600,760,4960,6305,6250, 40,9960,920,5360,6945,4880, 0,0,0,20,315,16.2K, 0,0,0,60,745,16.04K, 0,0,0,200,1430,16.1K, 0,520,40,1020,2590,14.11K, 0,1480,40,1980,3155,13.7K, 0,1800,20,1980,3325,13.05K, 0,1960,60,2400,3500,12.76K, 0,2480,240,2680,3630,12.52K, 0,4000,180,3020,3780,11.75K, 0,4760,240,3280,3935,11.04K, 40,6240,340,3820,4215,9295, 0,7480,360,4060,4525,8520, 0,0,0,0,90,16.34K, 0,0,0,0,235,16.47K, 0,0,0,20,340,16.06K, 0,160,0,220,775,15.92K, 0,320,20,440,935,15.62K, 0,440,0,500,1065,15.9K, 0,440,0,700,1025,15.66K, 0,520,40,740,1095,15.22K, 0,960,40,760,1145,15.2K, 0,1320,120,720,1285,14.99K, 0,1440,160,1020,1295,15.04K, 0,1760,180,880,1300,14.72K, 0,0,0,0,45,16.37K, 0,0,0,0,70,16.91K, 0,0,0,0,240,16.53K, 0,40,0,120,360,16.52K, 0,120,0,180,570,16.2K, 0,160,20,260,580,16.05K, 0,360,20,280,585,15.83K, 0,200,40,380,685,15.84K, 0,320,20,480,675,15.85K, 0,440,0,520,665,15.91K, 0,720,160,560,675,15.48K, 0,520,120,600,800,15.27K, 0,0,0,0,5,16.25K, 0,0,0,0,25,16.58K, 0,0,0,0,105,16.6K, 0,0,0,20,170,16.68K, 0,0,0,20,300,16.43K, 0,40,0,120,230,16.91K, 0,0,20,120,220,16.37K, 0,0,0,120,215,15.95K, 0,0,60,180,310,15.99K, 0,40,60,160,325,16.2K, 0,0,40,240,270,16.52K, 0,80,40,140,400,16.42K, 0,0,0,0,0,15.96K, 0,0,0,0,610,15.7K, 0,0,0,40,1565,14.92K, 0,40,0,280,2750,13.95K, 0,720,20,2020,5270,11.85K, 0,2080,40,4180,6935,9480, 0,3120,40,4140,7165,8460, 0,3880,100,4240,7660,8090, 0,4560,140,4860,7685,7100, 0,6800,100,5740,8295,5685, 0,8920,320,6120,8635,4065, 0,11.28K,480,7140,9155,1520, 40,12.44K,620,7900,9500,0, 0,0,0,0,490,15.43K, 0,0,0,20,1135,15.36K, 0,0,0,160,2245,14.36K, 0,400,0,1500,3985,12.74K, 0,1960,60,2580,5265,11.35K, 0,2800,100,3320,5240,10.62K, 0,2960,120,3580,5615,9675, 40,3120,60,3920,5945,9045, 0,4920,280,4300,6290,8005, 0,6400,300,4700,6630,6645, 40,8560,420,5540,6685,5090, 40,10.2K,640,5140,7240,3765, 0,0,0,20,545,16.15K, 0,0,0,40,995,15.4K, 0,0,0,240,1925,14.74K, 0,480,40,1240,3655,13.51K, 0,1440,80,2520,4635,11.15K, 0,2040,40,2820,5005,10.9K, 0,2880,40,3320,4935,10.52K, 0,3560,80,3120,5400,9845, 0,4840,220,3880,5320,8380, 0,5800,300,4440,5705,7465, 40,7760,460,4840,6260,5920, 40,9560,460,5060,6415,5165, 0,0,0,0,345,15.64K, 0,0,0,20,725,15.37K, 0,0,0,220,1285,14.67K, 0,360,0,780,2530,14.03K, 0,1040,20,2080,3100,12.64K, 0,1440,60,1900,3200,12.14K, 0,1680,40,2120,3410,11.64K, 0,1960,120,2360,3530,11.63K, 0,3560,140,2900,3790,10.78K, 0,4080,200,2940,3860,10.21K, 40,5840,380,3540,4110,8635, 40,6640,420,3660,4490,8390, 0,0,0,0,65,15.84K, 0,0,0,0,210,15.77K, 0,0,0,40,345,15.87K, 0,40,0,360,765,16.08K, 0,200,40,460,895,14.87K, 0,240,0,560,1045,14.72K, 0,320,40,580,1135,14.87K, 0,520,20,660,1160,14.08K, 0,800,0,720,1060,14.41K, 0,1000,40,1020,1305,13.94K, 0,1360,120,940,1265,13.84K, 40,1360,180,1080,1220,13.55K, 0,0,0,0,30,15.67K, 0,0,0,20,80,15.89K, 0,0,0,20,205,16.03K, 0,0,0,100,465,15.5K, 0,80,20,240,525,15.24K, 0,40,0,340,530,15.23K, 0,120,40,400,475,15.1K, 0,320,0,300,615,14.58K, 0,400,0,340,700,14.96K, 0,600,40,380,660,14.78K, 0,560,80,440,810,14.52K, 0,520,160,560,795,14.48K, 0,0,0,0,5,15.33K, 0,0,0,0,30,15.76K, 0,0,0,0,80,15.4K, 0,40,0,0,180,15.82K, 0,0,20,100,185,15.16K, 0,0,0,100,290,15.5K, 0,0,0,60,240,15.68K, 0,0,40,120,245,15.12K, 0,40,60,120,215,15.17K, 0,40,20,100,340,15.22K, 0,40,20,180,345,15.03K, 0,120,60,120,315,14.8K, 0,0,0,0,0,15.15K, 0,0,0,0,590,15.22K, 0,0,0,20,1525,14.51K, 0,40,0,340,2765,13.47K, 0,800,20,1700,5365,11.4K, 0,2080,60,3440,6730,9385, 0,3320,60,4020,6925,7955, 0,3320,100,4180,7125,7355, 0,4160,180,4700,7470,6715, 0,6160,220,5520,7780,5245, 0,8240,220,5600,8630,3665, 0,10.36K,440,7100,9010,1550, 0,12K,840,7500,9080,0, 0,0,0,0,425,14.6K, 0,0,0,40,1290,14.31K, 0,40,0,240,1975,14.04K, 0,320,20,1560,3865,12.22K, 0,1480,80,2500,4935,10.89K, 0,1920,20,3020,5215,9695, 0,2560,100,3340,5440,9490, 0,2840,100,3760,5860,8310, 0,4560,180,4040,6270,7420, 0,5520,220,4880,6700,6190, 0,8080,340,4980,6790,4525, 0,9160,660,5440,6835,3420, 0,0,0,0,495,15.03K, 0,0,0,0,895,14.3K, 0,0,0,280,1760,13.97K, 0,520,20,1080,3510,12.56K, 0,1520,40,2460,4450,10.7K, 0,2200,80,2820,4575,10.25K, 0,2400,60,2920,5010,9990, 0,2840,80,3260,5235,9495, 0,3720,140,3720,5460,8445, 0,5280,240,4040,5865,7465, 0,6680,420,4600,6085,5500, 80,8200,460,4800,6365,4785, 0,0,0,0,320,15.23K, 0,0,0,20,675,14.5K, 0,0,0,100,1225,14.24K, 0,240,0,820,2390,13.3K, 0,920,60,1720,3135,11.93K, 0,1480,20,1620,3545,12.17K, 0,1560,40,2260,3285,11.14K, 0,2440,20,1900,3570,10.9K, 0,3200,140,2660,3865,10.43K, 0,3800,100,2900,4090,9840, 0,4920,340,3500,4040,8530, 80,5880,340,3480,4130,7845, 0,0,0,0,55,15.33K, 0,0,0,0,250,14.78K, 0,0,0,20,415,14.83K, 0,120,0,220,810,14.41K, 0,240,40,320,920,14.26K, 0,280,20,560,935,13.93K, 0,240,40,600,975,14.35K, 0,440,80,700,1045,13.69K, 0,400,60,680,1080,13.26K, 0,720,40,1040,1310,13.67K, 0,1080,120,1180,1110,13.57K, 0,1360,100,900,1315,13.02K, 0,0,0,0,45,14.88K, 0,0,0,0,75,14.97K, 0,0,0,20,160,14.58K, 0,0,0,100,390,14.76K, 0,160,0,240,580,14.82K, 0,0,0,320,520,14.57K, 0,40,0,340,650,14.1K, 0,120,20,240,575,14.31K, 0,320,20,260,665,14.07K, 0,560,0,440,590,14.23K, 40,600,100,560,755,14.38K, 0,320,120,640,755,13.9K, 0,0,0,0,5,15.27K, 0,0,0,0,45,15.22K, 0,0,0,0,35,15.07K, 0,0,0,40,135,14.87K, 0,40,0,80,170,14.55K, 0,0,0,80,270,14.72K, 0,40,0,120,210,14.6K, 0,0,40,60,270,15.32K, 0,0,0,60,300,15.08K, 0,0,40,140,325,14.85K, 0,40,20,220,350,14.38K, 0,0,100,140,480,14.74K, 0,0,0,0,0,14.72K, 0,0,0,20,650,14.49K, 0,0,0,40,1490,13.62K, 0,40,0,300,2740,12.9K, 0,440,0,1800,4970,10.57K, 0,1800,40,3460,6540,8895, 0,2440,100,4200,6710,7840, 0,3520,80,3900,6965,7550, 0,3960,160,4200,7410,6255, 0,5480,220,5040,8090,5195, 0,7600,260,5660,8515,3635, 0,9720,540,6540,8525,1485, 0,10.64K,580,7600,9225,0, 0,0,0,0,365,14.01K, 0,0,0,80,1110,14.44K, 0,0,0,200,2050,13.67K, 0,400,40,1260,3765,11.59K, 0,1280,20,2300,5060,10.18K, 0,2160,40,2720,5020,9325, 0,2080,120,3360,5725,8695, 0,3040,120,3660,5515,8025, 0,4120,180,3980,6030,7400, 40,5840,200,4320,6125,6025, 0,7280,380,5240,6715,4460, 0,8240,440,6040,6705,3270, 0,0,0,0,420,13.77K, 0,0,0,60,890,13.83K, 0,0,0,220,1925,13.42K, 0,400,20,1040,3395,12.09K, 0,1640,0,2140,4405,10.68K, 0,2000,40,2460,4975,10.1K, 0,2360,20,2700,4900,9630, 0,2640,100,2940,5260,9520, 0,3520,100,3580,5285,8025, 40,4760,120,4160,5835,6940, 40,6360,340,4440,6460,5600, 0,7480,460,4980,6160,4455, 0,0,0,0,315,14.38K, 0,0,0,60,560,13.84K, 0,0,0,160,1305,13.62K, 0,240,20,680,2235,12.61K, 0,1000,0,1520,3080,11.86K, 0,1320,0,1940,3200,11.51K, 0,1560,40,1840,3520,11.06K, 0,1680,160,2320,3610,10.85K, 0,2760,180,2440,3890,9815, 0,2960,220,2900,4155,9515, 0,4400,360,3540,4100,8255, 40,5160,300,3680,4230,7705, 0,0,0,0,65,14.68K, 0,0,0,0,180,14.65K, 0,0,0,40,400,14.61K, 0,40,0,180,670,13.94K, 0,120,0,440,865,13.69K, 0,240,20,520,915,14.1K, 0,320,0,580,980,13.57K, 0,480,20,560,1040,13.75K, 0,520,40,780,1060,12.94K, 0,880,80,740,1175,13.28K, 0,960,80,940,1360,12.6K, 0,1120,200,940,1225,12.38K, 0,0,0,0,35,13.98K, 0,0,0,0,100,14.86K, 0,0,0,20,195,14.73K, 0,0,0,140,310,14.36K, 0,120,0,180,505,14.33K, 0,80,20,220,525,13.72K, 0,120,20,260,655,13.67K, 0,80,40,280,485,14.39K, 0,240,20,380,570,13.83K, 0,400,20,420,710,14K, 0,520,60,460,795,13.6K, 0,600,80,400,750,13.47K, 0,0,0,0,20,14.56K, 0,0,0,0,35,14.34K, 0,0,0,0,35,14.49K, 0,0,0,0,170,14.5K, 0,0,0,100,200,14.28K, 0,40,0,60,220,14.11K, 0,0,0,80,240,14.07K, 0,40,0,40,240,14.25K, 0,0,0,160,290,14.03K, 0,0,60,100,295,14.32K, 0,160,80,140,335,14.01K, 0,120,0,180,360,13.43K, 0,0,0,0,0,13.65K, 0,0,0,0,610,13.56K, 0,0,0,100,1390,13.61K, 0,0,0,360,2375,12.49K, 0,560,0,1500,5095,10.41K, 0,1960,40,3080,6470,8435, 0,2480,60,3580,6675,7325, 0,2880,80,4020,7200,6900, 0,3440,80,4120,7220,6295, 0,5160,40,4680,7660,4740, 0,6480,240,5460,8580,3490, 40,9040,420,6180,9025,1400, 40,10.72K,480,6900,9100,0, 0,0,0,0,430,14K, 0,0,0,20,1085,13.94K, 0,0,0,340,1905,12.99K, 0,680,0,1100,3670,11.25K, 0,1120,40,2360,4960,9735, 0,1440,60,2680,5020,8860, 0,2160,140,2760,5580,8560, 0,2840,80,3300,5680,8225, 40,3800,220,4100,5790,6985, 0,4960,240,4680,6420,5860, 0,6880,340,4880,6570,4500, 40,8200,280,5360,6880,3180, 0,0,0,0,350,13.95K, 0,0,0,60,860,13.5K, 0,0,0,200,1770,12.75K, 0,440,0,1080,3375,11.04K, 0,1440,40,2260,4265,10.69K, 0,1440,60,2540,4540,9875, 0,1760,120,3100,4995,8845, 0,2760,140,3120,5020,8900, 0,3600,200,3360,5465,8000, 0,4440,260,3920,5740,6755, 0,6720,240,4120,5880,5585, 0,7040,560,4760,6195,4290, 0,0,0,0,260,14.34K, 0,0,0,40,655,13.53K, 0,0,0,180,1285,13.34K, 0,280,0,880,2245,12.7K, 0,840,40,1500,3035,11.13K, 0,1320,60,1540,3170,11.23K, 0,1600,40,1880,3160,11.12K, 0,1640,60,2060,3585,10.31K, 0,2600,100,2360,3650,9990, 0,2760,220,2640,3960,9245, 0,4440,220,3200,3975,7965, 40,4600,440,3920,4160,7320, 0,0,0,0,90,14.52K, 0,0,0,0,165,14.22K, 0,0,0,60,360,13.66K, 0,0,0,200,580,13.3K, 0,40,0,500,835,13.46K, 0,360,0,400,940,12.91K, 0,400,0,380,1045,12.99K, 0,240,40,600,1005,13.06K, 0,600,20,580,1105,12.55K, 0,680,0,640,1370,12.82K, 0,960,140,860,1245,12.39K, 0,1000,120,960,1235,11.86K, 0,0,0,0,30,13.84K, 0,0,0,0,85,13.97K, 0,0,0,20,170,13.83K, 0,0,0,140,395,13.88K, 0,40,0,140,550,13.62K, 0,40,0,300,535,13.81K, 0,120,0,340,525,12.9K, 0,200,20,260,540,13.38K, 0,280,0,280,615,13.7K, 0,280,60,440,695,13.35K, 0,400,40,480,805,13.08K, 0,480,40,620,705,12.82K, 0,0,0,0,20,14.19K, 0,0,0,0,20,14.06K, 0,0,0,0,40,14.32K, 0,0,0,0,165,13.76K, 0,0,0,60,235,13.91K, 0,0,0,60,225,14.08K, 0,0,0,20,245,13.63K, 0,0,40,40,210,14.01K, 0,0,0,120,300,13.93K, 0,40,40,120,315,13.51K, 40,40,20,220,345,13.72K, 0,0,40,180,430,14.3K, 0,0,0,0,0,13.97K, 0,0,0,0,605,13.87K, 0,0,0,40,1455,13.47K, 0,0,0,360,2325,12.47K, 0,440,20,1520,4965,10.71K, 0,1520,80,3080,6405,8345, 0,2520,0,3140,6730,8075, 0,3120,80,4060,7170,7295, 0,3320,40,4100,7065,6420, 0,5040,280,4960,7825,4720, 0,6560,160,5680,8280,3530, 0,8560,420,6680,8435,1465, 0,10.56K,580,7220,8965,0, 0,0,0,0,400,13.89K, 0,0,0,20,1085,13.86K, 0,0,0,180,1835,13.09K, 0,360,0,980,3830,11.63K, 0,1240,20,2120,4875,9585, 0,1760,80,2440,4905,9310, 0,1840,60,2720,5325,8995, 0,2640,100,3200,5395,8270, 0,3240,140,3640,6030,7035, 40,4720,140,4400,6085,5885, 0,6440,320,4940,6840,4310, 0,8000,420,5200,6520,3200, 0,0,0,0,410,13.69K, 0,0,0,0,980,13.55K, 0,0,0,100,1760,12.89K, 0,480,20,1000,3125,12.3K, 0,840,0,2600,4240,10.37K, 0,1320,60,2060,4755,9700, 0,1920,40,2600,4850,9320, 0,2440,60,3280,4965,8765, 0,3480,160,3620,5270,7600, 0,4520,220,3620,5580,7010, 40,6360,400,4460,6255,5185, 40,6520,460,4880,6050,4510, 0,0,0,0,320,13.92K, 0,0,0,0,715,13.75K, 0,0,0,180,1220,13.9K, 0,360,20,660,2290,12.43K, 0,1080,40,1360,2730,11.2K, 0,1400,60,1340,3210,11.21K, 0,1320,0,1560,3510,10.72K, 0,1800,80,2060,3350,10.36K, 0,2640,140,2280,3730,9760, 0,2760,180,2920,3755,8845, 0,4560,160,3200,3965,8125, 40,5080,280,3760,4115,7220, 0,0,0,0,110,14.15K, 0,0,0,0,145,13.57K, 0,0,0,0,360,13.48K, 0,80,0,200,595,13.38K, 0,240,0,380,860,13.06K, 0,240,0,420,945,13.36K, 0,280,20,540,935,13.5K, 0,520,60,380,990,13.58K, 0,520,80,720,1070,13.07K, 0,760,60,720,1210,12.71K, 0,960,80,840,1235,12.7K, 0,1240,160,760,1215,12.06K, 0,0,0,0,40,14.32K, 0,0,0,0,75,14.02K, 0,0,0,20,145,14.33K, 0,40,0,100,420,13.45K, 0,0,20,200,530,14.05K, 0,80,0,300,525,13.43K, 0,120,20,300,630,13.39K, 0,240,40,320,525,13.38K, 0,280,80,320,600,13.16K, 0,160,20,420,635,13.5K, 0,520,40,400,735,13.22K, 0,520,60,600,760,13.23K, 0,0,0,0,15,14.2K, 0,0,0,0,35,14.33K, 0,0,0,0,90,14.25K, 0,0,0,0,130,13.66K, 0,0,0,40,180,13.9K, 0,0,0,80,230,13.99K, 0,0,0,60,205,13.93K, 0,0,20,60,235,13.96K, 0,0,20,40,270,13.74K, 0,40,0,120,355,14.28K, 0,80,20,120,360,14K, 0,0,20,300,310,13.56K, 0,0,0,0,0,13.9K, 0,0,0,0,535,13.65K, 0,0,0,60,1385,13.15K, 0,0,0,160,2520,12.31K, 0,480,0,1540,4755,10.96K, 0,1600,20,2980,6005,8720, 0,2040,100,3720,6440,8100, 0,2560,20,4180,7215,7025, 0,3160,120,4460,7020,6180, 0,5400,220,5180,7750,5080, 0,6320,180,5760,8350,3580, 0,9360,420,6480,8520,1375, 0,10.12K,500,6560,9270,0, 0,0,0,0,425,13.55K, 0,0,0,80,1040,13.64K, 0,0,0,240,1860,12.98K, 0,320,20,1140,3725,11.58K, 0,1160,20,2300,4625,9780, 0,1520,0,2520,5190,9165, 0,2320,80,2840,5255,9025, 0,2920,20,2980,5390,7860, 0,3600,180,3980,5790,7115, 0,5040,120,4200,6205,5910, 40,6600,380,4500,6680,4275, 0,8160,400,5100,6665,2990, 0,0,0,20,370,13.74K, 0,0,0,0,960,13.84K, 0,40,0,200,1610,12.75K, 0,480,0,1200,3105,11.94K, 0,1040,40,2320,4345,10.15K, 0,1600,0,1900,4690,9480, 0,2200,80,2640,4530,9145, 0,2400,80,3360,4980,8690, 0,3440,180,3740,5450,7825, 0,4040,280,4280,5610,7005, 0,6360,320,3900,6055,5115, 0,7400,380,4640,6035,4090, 0,0,0,0,280,13.92K, 0,0,0,60,630,13.95K, 0,0,0,220,1150,13.42K, 0,280,0,680,2335,12.59K, 0,880,0,1560,2710,11.32K, 0,960,80,1760,3160,11.05K, 0,1320,20,1720,3275,10.44K, 0,1600,60,2140,3445,10.51K, 0,2440,100,2280,3685,9335, 0,3320,180,2680,3775,9180, 0,4280,300,3660,4220,7880, 0,4920,280,3460,4170,7100, 0,0,0,0,85,13.77K, 0,0,0,0,165,13.9K, 0,0,0,40,315,14.38K, 0,0,0,240,620,13.42K, 0,160,20,380,930,13.15K, 0,160,20,600,980,13.34K, 0,160,20,560,925,13.22K, 0,440,20,700,900,12.92K, 0,600,0,480,1295,12.89K, 0,560,80,880,1300,12.65K, 0,680,100,980,1245,12.1K, 0,1120,140,980,1115,11.9K, 0,0,0,0,40,13.62K, 0,0,0,0,130,14.23K, 0,0,0,20,160,14.48K, 0,40,0,140,445,13.87K, 0,0,0,220,570,13.48K, 0,80,0,160,600,13.4K, 0,120,0,300,660,13.51K, 0,120,0,280,565,13.84K, 0,280,40,320,635,13K, 0,240,0,420,690,13.27K, 0,480,60,400,700,13.9K, 0,480,40,480,795,13.65K, 0,0,0,0,5,14.38K, 0,0,0,0,30,13.76K, 0,0,0,0,55,13.92K, 0,0,0,0,140,13.9K, 0,0,0,20,220,14.47K, 0,0,0,60,200,13.62K, 0,0,0,60,255,13.86K, 0,0,0,60,220,13.56K, 0,0,0,140,240,13.34K, 0,0,40,120,295,13.72K, 0,80,60,140,310,13.56K, 0,40,40,180,330,13.44K, 0,0,0,0,0,13.99K, 0,0,0,0,555,14.05K, 0,0,0,40,1360,13.69K, 0,0,0,320,2410,12.63K, 0,480,0,1720,4775,10.61K, 0,1640,80,3280,5995,8450, 0,2440,40,3640,6585,7665, 0,3000,20,3840,7010,6875, 0,3480,80,4120,6950,6230, 0,4760,60,5280,7940,5270, 0,6600,220,5820,8150,3535, 0,8800,500,6600,8885,1260, 40,11.04K,420,6780,8985,0, 0,0,0,0,400,13.99K, 0,0,0,0,1035,13.5K, 0,0,0,120,2135,12.88K, 0,240,0,1320,3740,10.84K, 0,1080,40,2600,4680,9885, 0,1480,60,2840,4750,9285, 0,2240,100,3040,5230,8815, 0,2520,80,3240,5375,8585, 0,3800,120,3700,6185,7285, 0,4880,280,4460,6280,5905, 40,6480,300,4740,6730,4290, 40,8040,500,5420,6485,3340, 0,0,0,0,400,13.89K, 0,0,0,40,890,13.52K, 0,40,0,140,1705,12.94K, 0,320,0,1240,3245,11.48K, 0,1120,20,2100,4515,9995, 0,1560,40,2580,4480,9545, 0,1880,60,2720,4600,9155, 0,2440,40,2820,4850,8785, 0,3520,40,3520,5270,7975, 0,4160,240,3800,5545,6930, 0,6240,260,4620,6030,5410, 0,7240,380,4900,6355,4480, 0,0,0,0,215,13.73K, 0,0,0,20,585,13.85K, 0,80,0,160,1185,12.83K, 0,320,0,680,2300,12.11K, 0,720,40,1500,3125,11.77K, 0,1280,60,1740,3070,10.82K, 0,1200,0,2000,3190,10.93K, 0,1560,80,1980,3445,10.35K, 0,2280,80,2520,3695,9560, 0,3680,120,2420,3705,9020, 0,4720,200,3380,3965,7905, 40,4920,280,3500,4335,7600, 0,0,0,0,90,13.98K, 0,0,0,0,180,13.82K, 0,0,0,40,320,14.28K, 0,40,0,180,625,13.58K, 0,160,40,400,885,13.63K, 0,280,0,500,975,13.48K, 0,160,20,540,905,12.97K, 0,480,20,580,980,12.19K, 0,600,0,740,1200,12.91K, 0,560,60,860,1055,12.61K, 0,800,100,920,1075,12.72K, 0,1080,140,960,1310,11.98K, 0,0,0,0,55,13.45K, 0,0,0,0,115,14.08K, 0,0,0,0,175,14.01K, 0,0,0,100,480,14K, 0,0,20,260,525,13.81K, 0,80,20,240,505,13.71K, 0,120,0,220,625,13.63K, 0,80,0,320,630,13.5K, 0,280,40,360,600,13.91K, 0,320,60,440,680,13.18K, 0,400,40,440,765,13.39K, 0,520,80,380,820,13.14K, 0,0,0,0,20,13.64K, 0,0,0,0,25,13.89K, 0,0,0,0,65,13.75K, 0,0,0,20,125,14.04K, 0,0,0,0,195,14.29K, 0,0,0,60,190,13.58K, 0,0,0,80,285,14.19K, 0,0,20,60,275,14.21K, 0,0,20,120,270,14.2K, 0,0,0,160,315,13.59K, 0,0,0,100,335,13.65K, 0,40,100,180,325,13.95K, 0,0,0,0,0,14.35K, 0,0,0,40,590,13.63K, 0,0,0,20,1430,13.37K, 0,0,0,180,2490,12.91K, 0,720,0,1560,4665,10.71K, 0,1840,40,3200,6010,8275, 0,2040,40,3540,6710,7550, 0,2560,80,4000,6890,7100, 0,3320,140,4560,7065,6090, 0,5160,220,4680,7705,4820, 40,6960,340,5400,8140,3585, 0,9320,640,6000,9005,1430, 0,10.84K,520,6540,9105,0, 0,0,0,0,415,13.76K, 0,0,0,0,990,13.55K, 0,0,0,240,2110,13.08K, 0,240,0,1180,3765,11.24K, 0,1280,40,2120,4860,9850, 0,1440,0,2900,4900,9285, 0,2320,120,2960,5090,8640, 0,2480,60,3140,5610,8395, 0,3640,100,3780,5995,6735, 0,4720,200,4420,6120,6145, 0,6960,420,4620,6395,4185, 0,7920,500,5260,6710,3370, 0,0,0,0,415,13.91K, 0,0,0,40,810,13.13K, 0,0,0,160,1655,13.25K, 0,400,0,1120,3235,11.86K, 0,1240,40,2080,4210,10.68K, 0,1400,20,2500,4645,9665, 0,1800,100,2600,4725,9285, 0,2360,120,2920,5040,8930, 0,3400,120,3640,5195,7795, 0,4720,240,3460,5600,6645, 0,5280,420,4760,6015,5210, 40,7600,300,4840,6285,4335, 0,0,0,20,215,13.95K, 0,0,0,40,520,13.8K, 0,40,0,180,1185,13.6K, 0,360,0,680,2235,12.55K, 0,800,40,1300,3090,11.72K, 0,1120,40,1800,3235,11.21K, 0,1440,40,1700,3270,10.77K, 0,1920,40,2100,3590,10.35K, 0,2400,120,2200,3670,9535, 0,3240,120,2620,3810,9260, 0,4800,160,2860,4265,8025, 0,5440,460,3800,3920,7415, 0,0,0,0,95,14.35K, 0,0,0,0,210,14.05K, 0,0,0,20,385,14.02K, 0,0,0,220,685,13.5K, 0,160,20,380,810,13.2K, 0,240,20,420,955,13.11K, 0,200,40,460,940,13.1K, 0,480,20,460,1070,12.75K, 0,440,20,680,1280,13.46K, 0,680,40,720,1110,12.69K, 0,1040,60,780,1270,12.04K, 0,960,80,880,1295,12.21K, 0,0,0,0,35,14.21K, 0,0,0,0,120,13.94K, 0,0,0,20,175,14.16K, 0,0,0,140,405,13.68K, 0,40,0,280,440,13.96K, 0,160,40,220,470,13.66K, 0,40,20,280,500,13.82K, 0,160,40,380,555,13.27K, 0,160,0,460,660,13.22K, 0,360,20,400,625,13.4K, 0,320,120,420,715,12.78K, 0,520,60,480,760,13.59K, 0,0,0,0,20,14.13K, 0,0,0,0,45,14.24K, 0,0,0,0,70,14.16K, 0,0,0,20,135,14.17K, 0,0,0,40,180,13.89K, 0,0,0,80,255,13.67K, 0,0,20,60,245,13.96K, 0,0,20,80,265,13.76K, 0,0,20,140,225,14.2K, 0,0,0,180,345,13.47K, 0,80,20,100,345,14.03K, 0,0,60,140,440,13.29K ) 440,40,1 48,24 2,165,61,553,492,0,MIDM 2,152,162,661,477,1,MIDM [Vehicle,Scenario1_0_1] [Time_stat,Vehicle] Table(Output_1,Vehicle_noch,Zone,Length_1,Scenario1_0_1,Period_1)( 0,0,0, 1029,85,50, 2521,226,130, 5196,454,235, 12.68K,1196,652, 20.3K,1870,998, 23.11K,2061,1119, 25.96K,2273,1303, 28.21K,2550,1409, 33.25K,2985,1622, 38.85K,3364,1849, 46.45K,4199,2283, 51.6K,4453,2531, 1014,93,47, 2593,222,116, 4996,483,266, 12.59K,1067,593, 20.22K,1802,957, 22.54K,2018,1110, 25.17K,2262,1214, 27.7K,2467,1339, 33.2K,2884,1599, 37.87K,3345,1777, 45.41K,3893,2171, 50.49K,4380,2485, 1006,82,59, 2455,213,116, 4948,396,225, 12.24K,1013,584, 19.41K,1727,935, 21.79K,1914,1090, 24.21K,2157,1167, 26.52K,2369,1320, 31.61K,2770,1519, 36.48K,3174,1831, 43.16K,3756,2174, 48.36K,4061,2362, 792,73,48, 2228,178,92, 4308,388,214, 10.78K,903,527, 17.23K,1541,852, 19.16K,1709,902, 21.36K,1826,1015, 23.59K,2058,1180, 27.63K,2389,1391, 32.11K,2798,1596, 38.29K,3422,1899, 42.33K,3674,2125, 643,66,25, 1633,140,66, 3276,283,158, 7925,654,413, 12.76K,1125,658, 14.42K,1214,722, 15.97K,1438,762, 17.62K,1544,855, 20.94K,1864,1043, 24.19K,2086,1149, 28.7K,2485,1336, 32.11K,2816,1533, 506,37,24, 1181,99,64, 2393,239,113, 6145,530,322, 9735,845,470, 11.13K,955,506, 12.02K,1144,624, 13.3K,1158,646, 15.94K,1363,743, 18.48K,1573,876, 21.97K,1952,1082, 24.25K,2109,1183, 331,17,22, 771,47,43, 1495,120,73, 3905,327,192, 6091,547,281, 6913,603,340, 7727,699,376, 8462,727,443, 9882,876,503, 11.69K,1028,548, 13.65K,1218,715, 15.58K,1356,751, 0,0,0, 303,18,18, 702,68,25, 1416,107,68, 3482,333,182, 5683,506,277, 6345,506,336, 7031,579,334, 7767,664,391, 9048,789,425, 10.63K,958,504, 12.51K,1121,631, 14.12K,1213,682, 220,13,10, 533,48,23, 1071,84,53, 2675,246,120, 4248,387,226, 4713,373,218, 5345,467,251, 5743,491,298, 6818,621,339, 8182,699,422, 9606,837,511, 10.49K,962,534, 195,21,13, 494,43,18, 993,73,57, 2510,201,131, 3942,377,220, 4604,391,207, 5108,467,263, 5575,473,290, 6678,597,321, 7513,664,370, 9155,788,442, 10.18K,863,507, 184,17,10, 483,36,25, 920,75,48, 2346,206,102, 3684,339,188, 4253,398,172, 4691,394,224, 5204,432,241, 6016,546,309, 6914,586,302, 8588,691,406, 9444,812,441, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 1421,140,80, 3646,316,199, 7365,636,374, 18.12K,1552,914, 29.24K,2516,1442, 32.67K,2855,1579, 36.56K,3189,1734, 40.14K,3474,1968, 47.14K,4359,2251, 54.46K,4818,2717, 65.34K,5762,3116, 72.73K,6318,3593, 1429,121,68, 3465,271,179, 7109,583,338, 17.74K,1501,835, 28.21K,2481,1372, 31.96K,2805,1577, 35.58K,3145,1714, 39.17K,3389,1849, 45.94K,4081,2339, 53K,4708,2635, 63.45K,5639,3075, 71.05K,6194,3409, 1392,125,68, 3421,291,149, 6522,575,331, 16.56K,1446,792, 26.62K,2367,1263, 29.81K,2510,1445, 32.99K,2955,1633, 36.02K,3277,1759, 42.91K,3780,2091, 49.6K,4236,2457, 59.79K,5351,2917, 66.06K,5973,3239, 1001,116,46, 2635,222,119, 5285,453,277, 13.28K,1197,642, 21.19K,1866,1032, 23.6K,2149,1164, 26.49K,2309,1291, 29.06K,2557,1414, 34.31K,2961,1624, 39.81K,3568,1950, 47.7K,4216,2201, 53.11K,4693,2680, 796,81,43, 1899,161,87, 3914,334,205, 9742,869,495, 15.54K,1397,734, 17.56K,1548,841, 19.46K,1717,965, 21.42K,1807,1042, 25.22K,2143,1244, 29.27K,2654,1401, 35.38K,3049,1694, 38.92K,3268,1944, 473,40,20, 1034,117,40, 2209,184,98, 5585,499,280, 9257,765,465, 10.24K,916,490, 11.5K,1004,522, 12.44K,1150,618, 14.62K,1254,733, 16.77K,1487,861, 20.48K,1840,978, 22.62K,1969,1126, 250,25,16, 589,60,29, 1216,92,59, 3054,266,147, 4810,443,243, 5441,477,263, 6114,519,293, 6685,598,330, 8124,673,395, 9139,803,439, 11K,945,524, 12.24K,1012,566, 0,0,0, 2460,230,101, 6273,556,284, 12.35K,1048,565, 31.12K,2610,1493, 49.91K,4377,2433, 55.62K,5049,2738, 61.9K,5537,2953, 68.3K,6053,3375, 80.87K,7039,3985, 93.06K,8325,4519, 111.7K,9779,5556, 124.2K,10.89K,6065, 1748,149,84, 4297,387,233, 8575,745,413, 21.96K,1941,1070, 35.52K,3105,1679, 39.61K,3397,1935, 43.93K,3890,2142, 48.12K,4237,2270, 56.99K,4919,2826, 65.42K,5828,3161, 78.92K,6936,3877, 87.71K,7555,4278, 1360,115,65, 3394,295,158, 6961,670,365, 17.29K,1497,822, 27.19K,2452,1353, 30.74K,2696,1518, 34.22K,3023,1634, 37.95K,3369,1838, 44.56K,3825,2141, 51.55K,4449,2524, 62.29K,5505,2927, 68.41K,5881,3293, 508,42,22, 1162,110,65, 2464,240,140, 6142,548,316, 9862,846,481, 11.19K,963,568, 12.54K,1058,599, 13.81K,1240,662, 16.14K,1445,830, 18.68K,1604,890, 22.26K,2034,1106, 24.95K,2150,1220, 97,7,7, 346,24,20, 638,56,28, 1652,141,75, 2660,222,117, 3002,241,132, 3265,288,170, 3521,314,185, 4264,398,210, 4876,444,235, 5792,553,300, 6493,528,341, 23,2,2, 63,8,2, 100,11,3, 289,27,11, 446,30,20, 537,46,25, 588,54,35, 620,57,32, 767,67,31, 792,75,42, 974,85,49, 1159,89,50, 5,0,0, 8,0,0, 19,1,1, 54,9,4, 82,4,5, 109,10,4, 105,8,5, 141,15,7, 173,15,10, 163,17,11, 195,17,10, 240,29,8, 0,0,0, 3013,259,147, 7464,621,375, 15K,1294,785, 37.24K,3195,1803, 60.15K,5356,2865, 66.95K,5832,3267, 74.32K,6519,3667, 81.64K,7248,4091, 97.16K,8615,4601, 111.5K,9876,5269, 134.2K,11.68K,6632, 149.2K,13.33K,7325, 2753,257,126, 7033,621,356, 14.15K,1256,656, 35.46K,2982,1776, 56.57K,5003,2676, 63.35K,5441,3047, 69.98K,6157,3359, 77.44K,6811,3862, 91.94K,7918,4458, 105.7K,9257,5283, 127.4K,10.88K,6152, 141K,12.23K,7001, 2534,235,127, 6511,529,366, 12.74K,1099,649, 31.94K,2748,1610, 51.92K,4560,2468, 57.68K,5097,2898, 64.1K,5624,3038, 70.76K,6268,3520, 83.44K,7360,4076, 96.24K,8529,4707, 115.5K,10.2K,5676, 128.2K,11.31K,6410, 2012,188,107, 5193,477,253, 10.09K,894,534, 25.48K,2218,1217, 40.55K,3553,2031, 45.43K,3902,2276, 50.79K,4357,2503, 55.54K,5039,2703, 65.92K,5751,3292, 76K,6649,3720, 91.03K,8086,4457, 101.2K,9030,5010, 667,78,34, 1640,147,91, 3298,286,169, 8285,721,394, 12.98K,1062,630, 14.75K,1262,718, 16.33K,1453,759, 18.01K,1646,867, 21.4K,1844,1044, 24.65K,2245,1264, 29.15K,2622,1422, 32.7K,2882,1478, 456,36,23, 1178,96,51, 2237,205,109, 5599,510,263, 8976,809,423, 10.22K,872,503, 11.13K,961,522, 12.35K,1050,606, 14.61K,1230,722, 16.83K,1526,815, 20.04K,1763,1026, 22.62K,1984,1153, 2,0,0, 3,0,0, 12,1,1, 36,3,6, 53,4,4, 56,2,5, 70,10,6, 77,5,4, 94,9,3, 97,7,5, 109,11,8, 155,9,5, 0,0,0, 14.32K,1256,693, 35.65K,3167,1712, 71.67K,6343,3498, 177.1K,15.6K,8712, 285.1K,25.01K,13.9K, 320.6K,27.99K,15.52K, 356.4K,31.01K,17.35K, 390.2K,33.79K,18.93K, 462.6K,40.59K,22.59K, 532.6K,46.96K,26.24K, 638.8K,55.95K,31.25K, 712.1K,62K,35.08K, 10.04K,927,489, 25K,2239,1242, 50.17K,4440,2494, 125.3K,10.95K,6162, 200.3K,17.46K,9893, 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15.58K,1309,779, 8306,663,393, 3465,317,162, 50.66K,4469,2417, 49.64K,4266,2443, 47.54K,4124,2307, 41.3K,3628,2084, 34.4K,3091,1696, 32.52K,2850,1600, 30.44K,2750,1446, 28.43K,2514,1380, 24.16K,2153,1157, 19.6K,1665,974, 13.18K,1152,676, 9008,795,431, 50.88K,4409,2508, 49.87K,4357,2494, 48.3K,4244,2343, 43.86K,3912,2173, 39.07K,3453,1962, 37.43K,3385,1813, 35.48K,3218,1770, 34.39K,3011,1656, 30.54K,2696,1466, 27.38K,2439,1332, 22.82K,1950,1109, 19.47K,1754,941, 51.27K,4496,2534, 50.73K,4400,2388, 49.2K,4363,2434, 45.95K,4114,2193, 41.92K,3590,2047, 40.83K,3603,1984, 39.73K,3381,1919, 38.26K,3384,1876, 36.21K,3161,1722, 33.52K,2990,1712, 29.58K,2632,1487, 27.26K,2500,1316, 51.26K,4413,2564, 50.7K,4378,2388, 50.19K,4411,2406, 48.11K,4180,2294, 46.12K,3932,2218, 44.99K,4073,2200, 44.11K,3845,2190, 43.21K,3814,2155, 41.66K,3710,1973, 40.54K,3518,1983, 37.8K,3369,1811, 36.71K,3197,1747, 13.95K,1247,703, 13.68K,1194,652, 13.33K,1177,696, 12.73K,1103,635, 10.42K,987,493, 8378,721,412, 7821,688,372, 7052,604,324, 6355,556,295, 4810,448,237, 3499,322,193, 1398,144,67, 0,0,0, 13.93K,1200,684, 13.42K,1215,648, 12.81K,1140,660, 11.24K,989,552, 9625,848,459, 9185,783,467, 8649,766,421, 8118,657,421, 7031,606,328, 6174,504,312, 4355,361,240, 3319,289,148, 13.8K,1223,661, 13.48K,1128,673, 12.91K,1139,639, 11.48K,1017,540, 9780,847,473, 9447,805,459, 8936,818,440, 8544,676,423, 7254,639,367, 6522,534,322, 4913,448,257, 3773,338,185, 13.89K,1193,682, 13.62K,1161,707, 13.06K,1065,626, 11.8K,1000,574, 10.26K,917,493, 9744,866,426, 9425,841,453, 8758,746,410, 7853,676,401, 7044,645,335, 5723,486,271, 4586,393,220, 14.08K,1207,722, 13.96K,1217,679, 13.85K,1224,707, 13.96K,1144,669, 13.85K,1233,664, 13.97K,1247,683, 14.19K,1236,726, 14.03K,1224,666, 13.82K,1278,666, 13.76K,1241,643, 13.75K,1259,660, 13.72K,1169,715, 13.99K,1263,701, 14.02K,1192,705, 13.93K,1214,738, 14.09K,1173,682, 14.07K,1255,700, 13.97K,1280,680, 13.93K,1236,670, 13.78K,1218,676, 13.89K,1211,697, 14K,1165,691, 13.78K,1250,662, 14.17K,1261,707, 14.14K,1210,649, 13.92K,1269,722, 13.85K,1227,671, 14.09K,1267,719, 14K,1299,717, 14.01K,1232,689, 13.93K,1244,661, 14.01K,1251,665, 13.85K,1196,715, 13.95K,1214,649, 13.91K,1267,666, 13.92K,1246,709, 72.53K,6427,3587, 71.55K,6161,3591, 68.99K,6109,3392, 66.33K,5696,3153, 54.48K,4781,2609, 43.74K,3887,2154, 40.09K,3514,1952, 36.61K,3190,1847, 32.65K,2887,1620, 25.42K,2249,1275, 18.23K,1525,926, 7375,626,336, 0,0,0, 71.62K,6390,3588, 69.49K,6006,3494, 65.3K,5779,3215, 55.1K,4902,2639, 44.31K,3881,2121, 40.92K,3645,2003, 37.37K,3255,1841, 34.25K,3000,1726, 26.73K,2343,1325, 19.73K,1795,946, 8943,792,446, 2086,169,87, 71.92K,6229,3432, 69.7K,6166,3376, 66.26K,5890,3231, 56.38K,4906,2715, 46.24K,4179,2285, 43.27K,3756,2213, 39.77K,3560,1886, 36.59K,3314,1805, 30.17K,2650,1481, 23.1K,2042,1110, 13.43K,1167,673, 6802,653,315, 72.24K,6458,3496, 70.48K,6234,3388, 66.95K,5891,3317, 59.7K,5195,3030, 51.83K,4541,2520, 48.76K,4313,2430, 46.48K,4082,2246, 43.99K,3770,2147, 38.61K,3479,1954, 33.29K,2925,1637, 25.21K,2184,1252, 19.81K,1759,1019, 72.01K,6491,3501, 71.35K,6186,3515, 68.85K,5925,3427, 63.56K,5560,3059, 57.38K,5002,2879, 55.56K,4879,2702, 53.61K,4913,2574, 51.11K,4547,2551, 47.88K,4106,2316, 44.06K,3846,2202, 37.45K,3242,1849, 33.77K,3072,1648, 72.85K,6408,3577, 71.7K,6225,3595, 70.53K,6272,3429, 67.16K,5990,3307, 63.11K,5624,3093, 62.91K,5487,3100, 61.26K,5397,2863, 60.96K,5268,2833, 57.96K,5102,2821, 55.94K,5025,2573, 52.55K,4523,2524, 49.88K,4538,2502, 72.3K,6203,3632, 72.29K,6229,3562, 71.45K,6372,3452, 69.87K,6142,3375, 67.84K,6046,3370, 67.15K,5831,3288, 66.6K,5922,3147, 66.01K,5846,3305, 64.74K,5736,3111, 63.68K,5513,3114, 62.24K,5442,3018, 60.4K,5398,2954, 124.2K,10.92K,6033, 121.7K,10.61K,5990, 117.8K,10.46K,5630, 111.5K,9836,5407, 92.49K,8201,4630, 74.14K,6481,3573, 68.4K,5916,3352, 62.43K,5403,3097, 55.76K,4948,2672, 43.73K,3841,2179, 31.36K,2744,1513, 12.37K,1060,614, 0,0,0, 123.1K,10.85K,6141, 120K,10.49K,5768, 115.8K,10.26K,5697, 102.4K,9071,5068, 89.2K,7967,4344, 84.66K,7444,4229, 80.09K,7163,3915, 76.15K,6603,3705, 67.33K,5975,3225, 58.24K,5021,2832, 45.31K,3985,2337, 36.14K,3233,1881, 122.5K,10.69K,6089, 121K,10.63K,6020, 117.2K,10.15K,5706, 106.7K,9294,5084, 96.86K,8582,4755, 93.5K,8174,4594, 89.09K,7941,4367, 86.35K,7665,4122, 80.21K,7084,3750, 72.2K,6327,3499, 61.96K,5397,3101, 55.61K,4713,2721, 123.3K,10.85K,6026, 122.7K,10.77K,5970, 122.2K,10.73K,5898, 117.9K,10.52K,5751, 113.4K,9970,5545, 112.9K,10.04K,5512, 111.5K,9766,5515, 110.2K,9737,5457, 108K,9561,5243, 105K,9471,5197, 102K,8979,4978, 99.87K,8766,4855, 124.3K,11.01K,6071, 123.3K,10.85K,5991, 123.3K,10.83K,5882, 122.1K,10.69K,5934, 121.6K,10.79K,5968, 120.8K,10.48K,6022, 121.5K,10.62K,5996, 120.6K,10.59K,5993, 120.3K,10.45K,5887, 119.6K,10.39K,5798, 118.5K,10.32K,5665, 117.8K,10.42K,5759, 123.7K,11.09K,6191, 123.7K,10.79K,6097, 123.7K,10.93K,6082, 124.3K,11K,6031, 124K,10.73K,5949, 123.3K,10.71K,6208, 123.5K,10.74K,6074, 123.6K,10.84K,6054, 122.8K,10.93K,6005, 123K,10.82K,6038, 123.5K,10.8K,5972, 123.7K,10.73K,6022, 123.7K,10.96K,6208, 124.7K,10.83K,6101, 124.2K,10.97K,6024, 124.2K,10.74K,5977, 124.4K,10.84K,6065, 124.2K,10.8K,5988, 124K,10.83K,6232, 124.3K,11.04K,5891, 123.6K,10.69K,6015, 123.7K,11.09K,6088, 123.6K,10.84K,6228, 123.7K,10.63K,5975, 149.5K,13.08K,7274, 146K,12.9K,7216, 142K,12.53K,6902, 134.1K,11.54K,6626, 111.7K,9963,5402, 88.96K,7773,4342, 81.62K,7273,3960, 74.51K,6515,3678, 67.04K,5990,3315, 51.93K,4634,2578, 37.34K,3316,1802, 14.85K,1248,712, 0,0,0, 146.4K,12.9K,7310, 141.9K,12.56K,6939, 134.8K,11.7K,6493, 113.4K,10.14K,5566, 92.19K,8006,4481, 85.46K,7539,4249, 78.27K,6804,3876, 71.28K,6241,3389, 57.05K,4876,2768, 43.08K,3753,2164, 22.2K,2021,1062, 7769,628,394, 145.5K,12.88K,7139, 142.3K,12.37K,7044, 136.2K,11.92K,6685, 116.7K,10.27K,5733, 97.94K,8540,4771, 91.07K,7972,4457, 84.89K,7525,4148, 78.71K,6770,3876, 65.86K,5644,3273, 52.26K,4669,2625, 33.26K,2862,1610, 20.59K,1789,993, 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6248,16,0, 10.49K,136,0, 13.09K,112,8, 0,0,0, 0,0,0, 0,0,0, 16,0,0, 152,0,0, 288,0,0, 392,0,0, 664,0,0, 856,0,0, 1520,0,0, 2320,8,0, 3232,8,0, 0,0,0, 0,0,0, 0,0,0, 8,0,0, 120,0,0, 192,0,0, 232,0,0, 264,0,0, 496,0,0, 712,0,0, 1112,0,0, 1440,24,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 8,0,0, 8,0,0, 72,0,0, 64,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 8,0,0, 48,0,0, 0,0,0, 0,0,0, 8,0,0, 40,0,0, 776,8,0, 2920,40,0, 3864,64,8, 4944,64,8, 6672,72,0, 9936,112,0, 13.91K,192,32, 20.99K,344,8, 27.7K,448,32, 0,0,0, 8,0,0, 56,0,0, 768,0,0, 2736,32,0, 3896,32,16, 4880,56,0, 6336,80,8, 9552,104,16, 13.3K,184,0, 20.68K,288,32, 26.19K,456,56, 0,0,0, 16,0,0, 72,0,0, 664,8,0, 2704,32,0, 3720,56,0, 4800,32,0, 6320,80,8, 9264,112,16, 13.34K,208,24, 20.1K,256,24, 24.9K,384,56, 0,0,0, 8,0,0, 64,0,0, 664,24,0, 2336,32,0, 2768,40,0, 3792,56,8, 4656,64,8, 6800,104,8, 9592,112,24, 14.23K,296,24, 18.23K,344,56, 0,0,0, 0,0,0, 0,0,0, 56,0,0, 488,0,0, 688,0,0, 928,8,0, 1296,0,0, 2184,16,0, 2960,16,0, 4816,56,0, 6400,24,0, 0,0,0, 0,0,0, 0,0,0, 48,0,0, 448,0,0, 536,0,0, 736,0,0, 1016,0,0, 1608,0,0, 2488,24,0, 3752,24,0, 5128,40,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 8,0,0, 1328,0,0, 6552,8,0, 9680,16,0, 13.21K,104,8, 17.65K,80,0, 28.29K,136,0, 40.92K,312,0, 65.82K,600,24, 85.07K,920,48, 0,0,0, 0,0,0, 16,0,0, 1168,0,0, 5448,8,0, 8056,32,0, 10.72K,72,0, 14.25K,80,0, 23.1K,184,0, 32.87K,336,0, 52.52K,520,24, 68.02K,712,8, 0,0,0, 0,0,0, 24,0,0, 1144,8,0, 5536,16,0, 7880,8,0, 10.84K,88,8, 14.67K,112,0, 22.78K,128,0, 32.28K,320,0, 52.23K,440,24, 65.92K,808,24, 0,0,0, 0,0,0, 8,0,0, 1000,0,0, 4512,0,0, 6480,16,0, 8512,32,0, 11.59K,64,0, 18.13K,152,0, 25.74K,336,16, 40.86K,456,0, 52.02K,680,24, 0,0,0, 0,0,0, 0,0,0, 96,0,0, 648,0,0, 1008,0,0, 1224,16,0, 1776,0,0, 3088,16,0, 4120,32,0, 6992,48,0, 8872,80,0, 0,0,0, 0,0,0, 0,0,0, 32,0,0, 192,8,0, 344,0,0, 608,0,0, 792,0,0, 1280,0,0, 1864,8,0, 3008,40,0, 3768,40,0, 0,0,0, 0,0,0, 0,0,0, 32,0,0, 112,0,0, 152,0,0, 392,0,0, 464,0,0, 536,0,0, 848,0,0, 1296,8,0, 1592,24,0, 0,0,0, 360,8,0, 2368,32,0, 8392,312,8, 27.67K,1768,304, 44.84K,3416,880, 49.03K,3896,1056, 53.32K,4520,1320, 57.46K,5192,1552, 64.38K,6200,1984, 71.6K,7632,2680, 78.91K,9160,3736, 84.17K,9848,4232, 240,0,0, 2288,80,0, 7776,296,16, 26.21K,1408,288, 42.09K,3144,744, 45.91K,3840,928, 50.41K,4320,1232, 53.74K,4912,1496, 60.09K,5856,2104, 66.31K,7120,2640, 72.94K,8552,3384, 78.22K,9224,4056, 264,0,0, 2368,40,0, 7776,240,24, 25.81K,1440,264, 40.69K,3032,736, 44.87K,3648,976, 48.64K,4264,1240, 52.06K,4616,1432, 57.87K,5776,1856, 64.34K,6768,2424, 70.59K,8240,3176, 75.02K,8800,3680, 520,0,0, 3568,96,0, 10.6K,408,40, 33.08K,2024,600, 50.33K,4120,1304, 56.1K,4960,1528, 60.55K,5456,1792, 64.86K,5928,2176, 73.07K,7592,2880, 80.07K,8760,3496, 89.41K,10.56K,4576, 94.34K,11.29K,5456, 32,0,0, 992,0,0, 4328,96,8, 14.67K,752,88, 23.23K,1704,344, 25.48K,1976,424, 27.95K,2344,624, 29.61K,2736,632, 32.13K,3192,904, 35K,3848,1320, 37.9K,4824,1768, 40.04K,5184,2208, 0,0,0, 312,0,0, 1744,0,0, 7888,360,40, 12.53K,752,112, 13.99K,1056,152, 15.1K,1144,192, 15.98K,1392,264, 17.97K,1768,416, 19.39K,2056,520, 21.34K,2472,768, 22.14K,2704,1024, 0,0,0, 8,0,0, 152,0,0, 960,48,0, 1960,120,8, 2136,104,8, 2336,144,16, 2464,120,0, 2544,160,24, 3264,264,24, 3600,328,72, 3368,336,88, 0,0,0, 0,0,0, 16,0,0, 64,0,0, 864,8,0, 1744,80,0, 2256,56,0, 2368,64,8, 2752,88,8, 3400,128,8, 3848,200,16, 4912,312,40, 5320,312,56, 0,0,0, 8,0,0, 48,0,0, 736,8,0, 1728,32,8, 1952,56,16, 2120,96,16, 2536,88,0, 2904,160,16, 3480,208,0, 4056,248,64, 4472,328,48, 0,0,0, 0,0,0, 56,0,0, 672,8,0, 1592,32,0, 1784,24,0, 2064,56,8, 2200,88,8, 2632,128,16, 2968,128,24, 3488,240,48, 3608,296,48, 0,0,0, 80,0,0, 424,0,0, 1776,72,0, 3184,168,16, 3576,232,56, 3768,224,48, 4104,296,72, 4384,432,80, 5064,488,80, 5552,552,160, 5880,600,208, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 40,0,0, 680,0,0, 3104,72,0, 13.17K,648,24, 23.28K,1544,112, 25.66K,1896,240, 29.06K,2200,240, 31.97K,2464,432, 35.99K,3168,648, 40.79K,3976,792, 47.02K,4856,1200, 50.69K,5360,1512, 16,0,0, 448,0,0, 2576,64,0, 11.55K,408,0, 20.19K,1240,152, 22.6K,1608,152, 25.01K,1800,264, 26.86K,2008,320, 31.32K,2720,480, 34.96K,3320,712, 40.08K,4112,984, 42.61K,4480,1224, 0,0,0, 464,0,0, 2280,48,0, 10.44K,528,24, 18.17K,1200,88, 20.54K,1336,96, 22.66K,1752,184, 24.58K,2024,296, 28.3K,2344,440, 31.87K,2904,632, 36.17K,3872,904, 39.07K,4360,1112, 40,0,0, 656,0,0, 2672,80,0, 9920,616,0, 16.59K,1192,200, 17.86K,1424,320, 20.22K,1632,312, 21.66K,1872,352, 24.73K,2304,488, 27.26K,2912,640, 30.63K,3496,1032, 32.26K,3872,1384, 8,0,0, 56,0,0, 840,0,0, 3824,144,0, 6872,392,0, 7624,472,16, 8376,568,48, 9104,656,48, 9912,912,120, 11.3K,1144,160, 12.82K,1344,256, 13.22K,1424,408, 0,0,0, 0,0,0, 240,0,0, 1816,24,0, 3328,120,8, 3672,240,0, 4208,304,0, 4552,320,40, 5224,400,40, 5568,552,32, 6400,648,72, 6744,672,88, 0,0,0, 16,0,0, 80,0,0, 440,8,0, 1032,40,0, 1224,24,0, 1288,64,0, 1464,96,0, 1800,120,32, 2016,144,16, 2288,216,24, 2480,208,24, 0,0,0, 24,0,0, 888,8,0, 4384,72,0, 17.77K,896,32, 28.79K,2008,248, 31.84K,2624,456, 35.02K,3040,408, 37.34K,3336,648, 42.19K,4176,928, 46.77K,4952,1256, 52.56K,6024,1960, 56.14K,6760,2176, 8,0,0, 296,0,0, 1472,8,0, 6512,376,8, 11.55K,736,56, 12.98K,968,144, 14.18K,1056,104, 15.6K,1336,120, 17.97K,1600,328, 20.15K,1888,440, 22.89K,2552,680, 24.45K,2592,800, 0,0,0, 208,8,0, 976,16,0, 3976,176,0, 6592,464,32, 7496,576,72, 8064,632,96, 9088,720,104, 10.24K,912,136, 11.68K,1088,240, 13.13K,1392,384, 14.27K,1440,504, 0,0,0, 0,0,0, 56,0,0, 408,16,0, 672,56,0, 736,72,0, 768,40,0, 768,72,8, 880,112,8, 936,120,24, 1056,144,56, 1104,184,40, 0,0,0, 24,0,0, 96,0,0, 416,24,0, 696,56,0, 744,64,8, 760,64,8, 848,88,0, 936,136,16, 992,160,32, 936,136,40, 1096,112,32, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 336,0,0, 3968,72,16, 18.14K,440,8, 73.91K,3400,344, 129.6K,8280,1160, 145.5K,9728,1848, 161.8K,11.63K,2296, 175.7K,13.14K,2704, 204.4K,17.22K,4168, 230.5K,20.54K,5144, 264.8K,25.27K,7480, 286.6K,28.84K,9320, 224,0,0, 3240,64,0, 14.32K,320,8, 62.47K,2792,232, 109.1K,6712,968, 121.8K,7920,1304, 134.8K,9544,1728, 147.4K,10.87K,2096, 171.3K,13.82K,3152, 192.2K,17.06K,4520, 222.5K,20.87K,6104, 238K,23.94K,7568, 216,0,0, 2856,32,0, 12.26K,320,8, 53.78K,2448,280, 93.25K,5808,816, 104.7K,6968,1112, 115.9K,8368,1528, 127.3K,9744,1904, 145.8K,12.33K,2888, 163.4K,14.92K,3536, 186.4K,18.79K,5296, 200.3K,21.05K,6528, 40,0,0, 1256,16,0, 6288,104,0, 32.1K,1280,48, 58.51K,2984,368, 63.72K,3792,432, 73.64K,4472,576, 78.78K,5416,816, 91.69K,6952,1296, 102.3K,8208,1704, 117.2K,10.82K,2632, 124.9K,12.46K,3120, 0,0,0, 72,0,0, 768,0,0, 4664,160,0, 8160,320,32, 9896,488,16, 10.64K,672,24, 11.66K,752,88, 13.2K,1040,128, 15.18K,1240,200, 16.81K,1616,272, 18.35K,1864,352, 0,0,0, 112,0,0, 424,8,0, 1952,72,0, 3008,248,8, 3312,232,24, 3448,288,40, 3952,360,24, 4360,408,64, 4808,552,88, 5256,680,168, 5712,624,256, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 8,0,0, 8,0,0, 0,0,0, 8,0,0, 16,0,0, 32,0,0, 64,0,0, 96,0,0, 0,0,0, 352,0,0, 4992,32,0, 24.73K,608,8, 114.1K,5032,472, 212.6K,12.13K,1608, 243.3K,14.47K,2072, 274.1K,16.92K,2832, 301.1K,19.96K,3424, 358K,26.3K,4944, 407.8K,32.25K,7080, 480.6K,40.7K,9832, 524.4K,46.73K,12.37K, 240,0,0, 3784,32,0, 17.96K,480,0, 81.62K,3280,264, 148.1K,8608,1344, 170.9K,10.41K,1664, 190.2K,12.54K,2216, 209.5K,14.38K,2472, 246.3K,18.54K,3832, 279.5K,22.99K,5336, 325.5K,29.03K,7328, 350.8K,32.76K,9776, 216,0,0, 3320,48,0, 16.48K,400,32, 72.81K,3328,344, 130.3K,7736,976, 148.2K,9368,1336, 164.6K,11.25K,1824, 180.8K,13.06K,2216, 211.8K,16.44K,3384, 238.5K,20.16K,4720, 275K,25.75K,6480, 297.7K,29.19K,8344, 160,0,0, 1928,24,0, 9152,216,8, 42.73K,1576,192, 75.98K,4160,600, 87.24K,5176,856, 96.28K,6280,960, 106.4K,7680,1352, 122.7K,9848,1928, 137.1K,11.9K,2608, 156.7K,14.85K,3752, 166.6K,17.51K,4952, 0,0,0, 144,0,0, 520,0,0, 2904,112,0, 5312,344,32, 6168,440,24, 6776,400,56, 7520,536,88, 8432,624,120, 9288,776,184, 10.5K,976,240, 11.56K,1232,256, 0,0,0, 0,0,0, 136,0,0, 800,56,0, 1664,56,0, 2008,96,8, 2224,112,8, 2328,160,8, 2664,232,24, 3104,288,32, 3608,376,40, 3624,368,56, 0,0,0, 0,0,0, 16,0,0, 160,0,0, 312,8,0, 288,24,0, 392,24,0, 440,16,0, 512,32,0, 528,80,0, 552,56,0, 584,56,0, 0,0,0, 0,0,0, 28,0,0, 260,0,0, 1892,44,8, 4420,164,8, 5124,164,16, 6224,188,24, 6824,248,52, 8664,388,60, 10.4K,492,116, 12.52K,680,112, 14.18K,824,116, 0,0,0, 24,0,0, 256,0,0, 2144,40,4, 4444,120,4, 5284,232,28, 6324,212,24, 6960,236,24, 8768,440,52, 10.14K,484,72, 12.52K,648,120, 13.74K,816,152, 0,0,0, 28,0,0, 216,8,0, 2040,28,0, 4332,124,12, 5168,136,24, 6072,240,28, 6948,308,32, 8768,348,44, 9800,492,88, 11.88K,676,116, 13.29K,796,172, 0,0,0, 28,0,0, 284,8,0, 1968,48,4, 4380,144,16, 4960,176,16, 6008,188,44, 6684,268,32, 7656,340,52, 9092,456,68, 10.88K,692,84, 12.08K,788,164, 0,0,0, 32,0,0, 280,4,0, 1716,36,0, 3544,140,8, 4084,124,12, 4800,168,4, 5440,252,36, 6460,368,52, 7256,464,48, 8528,548,96, 9148,716,140, 0,0,0, 28,0,0, 192,4,0, 1348,32,8, 2760,104,16, 3264,100,20, 3620,172,24, 4140,188,28, 4936,240,20, 5560,312,40, 6552,492,100, 6828,500,132, 8,0,0, 32,0,0, 136,4,0, 944,24,0, 1676,56,16, 2084,88,8, 2240,124,28, 2468,172,48, 2896,216,16, 3368,256,28, 3920,280,88, 4156,364,84, 0,0,0, 0,0,0, 4,0,0, 36,4,0, 412,20,0, 868,40,0, 1140,36,4, 1272,36,8, 1516,60,4, 1776,68,12, 2304,88,8, 2712,120,24, 3100,160,36, 0,0,0, 4,0,0, 52,0,0, 376,20,0, 828,32,12, 980,28,0, 1224,36,0, 1264,36,4, 1648,88,8, 2000,88,12, 2524,136,28, 2764,172,32, 0,0,0, 12,0,0, 64,0,0, 364,8,0, 824,32,0, 1020,32,8, 1184,44,8, 1312,36,16, 1656,80,12, 1936,116,24, 2420,132,32, 2624,176,48, 0,0,0, 0,0,0, 52,0,0, 404,12,4, 868,28,8, 1016,36,0, 1104,44,4, 1276,48,4, 1712,76,12, 1872,120,16, 2472,104,36, 2636,180,20, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 4,0,0, 168,0,0, 1728,8,0, 4860,92,12, 6172,128,4, 7284,140,12, 8752,180,20, 11.68K,360,28, 14.05K,428,48, 17.7K,680,72, 20.28K,792,100, 0,0,0, 12,0,0, 116,0,0, 1596,4,4, 4600,68,12, 6256,104,12, 7220,152,12, 8788,196,24, 10.94K,304,44, 13.75K,400,60, 17.35K,612,44, 20.33K,852,92, 0,0,0, 12,0,0, 132,0,0, 1560,20,0, 4480,96,0, 5552,76,4, 6720,136,20, 8084,200,12, 10.76K,300,24, 13.26K,372,32, 16.83K,640,56, 18.95K,800,48, 0,0,0, 4,0,0, 76,0,0, 1096,8,0, 3468,60,0, 4464,72,4, 5536,68,8, 6588,120,16, 8916,164,24, 10.78K,264,12, 14.22K,436,32, 16.15K,536,88, 0,0,0, 4,0,0, 40,0,0, 1008,8,0, 2928,44,8, 3724,48,8, 4688,84,0, 5452,72,8, 7120,148,8, 8916,272,8, 11.18K,420,32, 12.6K,436,48, 0,0,0, 0,0,0, 36,0,0, 496,0,0, 1768,28,0, 2188,20,8, 2624,20,8, 2944,76,0, 3964,76,8, 4840,144,12, 6488,256,16, 7240,300,32, 0,0,0, 0,0,0, 20,0,0, 376,0,0, 956,0,0, 1268,20,0, 1444,32,0, 1788,48,0, 2352,60,4, 2652,96,16, 3396,132,8, 3744,120,16, 0,0,0, 0,0,0, 16,0,0, 280,0,0, 3316,56,0, 8116,204,40, 10.13K,312,28, 11.9K,308,36, 13.91K,404,28, 17.46K,668,76, 21.27K,844,100, 26.86K,1084,140, 30.64K,1436,216, 0,0,0, 28,0,0, 148,0,0, 2368,20,4, 6100,140,12, 6832,172,32, 8468,228,12, 9684,284,16, 12.36K,364,40, 14.78K,588,68, 19.02K,776,92, 21.74K,1040,200, 0,0,0, 20,0,0, 172,0,0, 1952,24,0, 4716,140,16, 5804,124,16, 6964,168,12, 8252,272,28, 10.17K,348,60, 12.81K,472,60, 15.79K,728,104, 17.58K,784,152, 0,0,0, 8,0,0, 24,0,0, 480,8,0, 1376,24,4, 1864,28,0, 2420,40,0, 2740,56,4, 3728,76,4, 4616,100,0, 6048,188,28, 7304,232,32, 0,0,0, 0,0,0, 20,0,0, 224,0,0, 624,0,4, 712,4,0, 752,32,4, 944,28,4, 1220,32,8, 1468,52,8, 1816,96,12, 2012,96,16, 0,0,0, 0,0,0, 0,0,0, 20,0,0, 64,0,0, 88,0,0, 104,0,0, 100,0,0, 176,0,0, 180,0,0, 288,4,0, 380,8,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 16,0,0, 24,0,0, 16,0,0, 28,0,0, 56,4,0, 48,4,0, 64,4,0, 76,4,0, 0,0,0, 4,0,0, 60,0,0, 444,12,0, 3968,56,0, 9996,236,20, 12.2K,276,32, 14.42K,400,60, 17.25K,520,64, 22.38K,720,100, 26.98K,1024,156, 35.61K,1356,232, 39.31K,1940,304, 8,0,0, 104,4,0, 392,8,0, 3760,72,8, 9080,244,28, 11.53K,268,20, 13.56K,384,36, 15.95K,504,52, 21.01K,680,92, 26.12K,964,160, 33.07K,1296,248, 37.6K,1592,308, 4,0,0, 64,0,0, 392,16,0, 3608,104,8, 9216,184,16, 10.8K,292,40, 13.06K,356,56, 15.08K,464,92, 19.88K,660,84, 24.18K,936,148, 30.86K,1300,228, 35.41K,1580,264, 16,0,0, 72,0,0, 404,0,4, 2564,32,12, 6300,152,20, 7864,180,48, 9664,248,40, 11.06K,420,52, 14.66K,436,100, 18.14K,724,112, 23.64K,904,216, 26.84K,1140,252, 0,0,0, 4,0,0, 80,0,0, 996,12,4, 2608,24,8, 3164,64,8, 3656,72,0, 4488,108,8, 5924,196,20, 7300,280,12, 8924,360,48, 9944,504,48, 0,0,0, 8,0,0, 88,4,0, 708,4,0, 1936,40,4, 2476,52,8, 2812,80,4, 3464,116,16, 4396,132,20, 5212,256,16, 6320,272,28, 7148,396,40, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 8,0,0, 0,0,0, 4,0,0, 140,0,0, 1208,8,0, 12.98K,204,8, 35.44K,632,60, 44.24K,988,104, 53.77K,1120,104, 62.99K,1580,104, 84.56K,2336,228, 106K,3220,380, 138.1K,4956,644, 161.7K,6080,868, 4,0,0, 128,0,0, 984,4,0, 10.21K,152,4, 26.94K,488,60, 34.24K,704,80, 41.38K,1060,124, 48.99K,1188,160, 64.09K,1868,224, 80.15K,2348,396, 104.1K,3828,520, 119.8K,4776,644, 0,0,0, 76,0,4, 992,0,0, 9816,140,20, 26.46K,564,52, 32.7K,740,96, 39.64K,956,84, 47.27K,1156,120, 61.96K,1844,276, 77.86K,2352,336, 100.5K,3676,544, 115.5K,4724,624, 0,0,0, 120,4,0, 880,4,0, 7564,144,8, 19.99K,436,36, 24.98K,556,48, 30.1K,676,68, 35.09K,1000,104, 45.83K,1320,212, 58.05K,1896,252, 73.7K,2924,436, 84.62K,3524,588, 0,0,0, 12,0,0, 100,0,0, 1376,12,0, 3620,68,4, 4296,100,4, 5168,132,8, 5744,160,16, 7508,228,28, 8888,364,36, 11.2K,480,48, 12.35K,552,68, 0,0,0, 4,0,0, 44,4,0, 524,4,0, 1452,20,4, 1708,64,12, 2012,48,4, 2320,68,4, 2808,76,12, 3436,100,24, 3936,208,24, 4556,240,40, 0,0,0, 4,0,0, 12,0,0, 228,0,0, 572,8,8, 620,12,0, 632,44,4, 732,44,8, 996,36,12, 1080,56,12, 1124,68,12, 1264,108,8, 0,0,0, 724,12,0, 2676,180,20, 5052,420,120, 8288,1092,604, 9404,1696,1036, 9728,1756,1108, 10.29K,1904,1224, 10.33K,2052,1492, 10.47K,2200,1688, 10.74K,2236,1832, 11.52K,2416,2256, 11.63K,2672,2464, 784,32,0, 2564,128,16, 4756,416,140, 8188,1088,512, 9316,1660,940, 9300,1632,1188, 9852,1812,1260, 10.07K,1848,1364, 10.28K,2144,1668, 10.59K,2232,1716, 10.85K,2308,2184, 10.84K,2584,2196, 788,24,0, 2380,168,32, 4624,412,96, 7824,1024,588, 9180,1704,932, 9368,1692,1056, 9532,1872,1120, 9804,1836,1368, 10.14K,2068,1592, 10.39K,2284,1844, 10.57K,2284,2176, 10.91K,2412,2256, 992,48,4, 2836,208,52, 4828,484,232, 8496,1208,668, 9992,1668,1052, 9980,1732,1132, 10.45K,1840,1344, 10.29K,2000,1448, 10.63K,2184,1752, 10.91K,2252,1876, 10.58K,2472,2120, 10.81K,2736,2320, 508,4,0, 1824,80,12, 3416,272,52, 5368,752,352, 6056,1100,684, 6236,1284,752, 6212,1292,768, 6372,1292,1000, 6796,1412,1244, 6636,1464,1312, 6784,1560,1500, 6888,1680,1584, 236,0,0, 1084,44,0, 2232,172,44, 4140,540,160, 4872,776,348, 4924,864,432, 5116,1000,536, 5088,956,624, 5320,940,712, 5488,1212,824, 5512,1220,1036, 5856,1288,1088, 12,0,0, 196,0,0, 644,8,4, 1876,124,16, 2400,268,72, 2560,320,80, 2712,308,100, 2856,380,140, 3056,488,200, 3080,480,184, 3312,520,300, 3476,576,364, 0,0,0, 12,0,0, 100,0,0, 328,0,0, 944,52,16, 1424,116,40, 1532,140,40, 1724,152,56, 1812,184,60, 1856,196,44, 2204,280,84, 2356,300,176, 2624,296,168, 16,0,0, 88,0,0, 316,8,0, 880,44,8, 1028,124,20, 1060,120,28, 1100,140,64, 1020,168,64, 1180,192,76, 1148,224,96, 1144,208,132, 1164,224,172, 8,0,0, 72,4,0, 300,12,0, 756,52,8, 756,92,28, 768,132,36, 776,188,32, 752,168,76, 788,188,60, 848,200,96, 788,208,120, 724,216,156, 36,0,0, 224,4,0, 560,28,0, 980,100,36, 940,184,112, 996,196,132, 1004,252,112, 1008,228,124, 1028,240,184, 984,220,192, 1020,300,244, 1016,304,288, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 244,4,0, 1412,76,0, 3128,244,12, 6248,716,272, 7728,1080,632, 8304,1216,608, 8612,1212,680, 8776,1360,780, 9468,1548,888, 10.01K,1640,1140, 10.64K,1984,1328, 10.9K,2068,1460, 232,4,0, 1184,48,0, 2620,192,16, 5304,664,176, 6688,936,428, 7096,1096,532, 7520,1184,624, 8012,1256,668, 8368,1356,824, 8904,1580,872, 9204,1612,1280, 9652,1740,1100, 224,4,0, 1224,44,4, 2492,176,16, 5184,576,184, 6756,932,404, 7184,952,572, 7516,1120,596, 7844,1184,656, 8404,1348,776, 8684,1440,952, 9280,1536,1164, 9576,1720,1260, 216,0,0, 976,56,0, 1796,136,44, 3572,444,232, 4252,672,340, 4496,696,412, 4504,768,488, 4544,864,520, 4804,924,588, 5172,1000,764, 5280,1140,752, 5480,1088,896, 76,0,0, 508,12,0, 1148,76,8, 2564,248,56, 3244,436,156, 3600,508,192, 3636,524,220, 3960,580,308, 4252,644,380, 4396,732,412, 4612,848,532, 4756,896,604, 24,0,0, 196,8,0, 716,8,0, 1632,164,28, 2152,268,84, 2324,332,104, 2452,332,116, 2508,392,132, 2728,408,192, 2936,444,312, 3272,632,300, 3372,560,380, 12,0,0, 100,8,0, 332,4,0, 1008,88,12, 1416,144,44, 1572,200,52, 1788,180,48, 1888,168,72, 2140,232,80, 2300,276,108, 2568,316,128, 2592,332,156, 0,0,0, 492,0,0, 2172,84,16, 4204,296,44, 6140,880,356, 6948,1352,780, 6812,1376,904, 7348,1480,992, 7492,1520,1220, 8016,1644,1340, 8528,1700,1584, 8512,1804,1784, 8760,1932,2040, 156,4,0, 768,48,0, 1704,120,8, 3288,424,148, 3728,648,296, 3932,648,316, 3952,728,420, 4100,800,480, 4244,820,528, 4348,932,612, 4540,888,680, 4788,1024,796, 84,4,0, 516,16,0, 1248,88,4, 2436,256,76, 3140,416,200, 3176,500,188, 3424,548,216, 3392,612,304, 3664,652,316, 3664,696,408, 3948,708,528, 3760,860,540, 12,0,0, 56,0,0, 148,4,4, 184,24,16, 276,36,12, 444,52,16, 312,64,28, 532,56,40, 544,68,60, 628,92,56, 692,80,68, 680,76,60, 0,0,0, 68,0,0, 136,8,0, 244,28,8, 228,36,16, 276,44,20, 340,60,28, 272,28,48, 336,48,48, 304,40,32, 420,64,52, 352,64,88, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 1708,36,0, 7572,352,52, 15.79K,1160,192, 28.19K,3520,1360, 34.85K,5584,2748, 36.3K,6092,3292, 37.43K,6272,3852, 38.17K,6756,4276, 40.4K,7444,4872, 41.82K,7792,5884, 43.2K,8712,7072, 44.48K,8916,7620, 1416,40,0, 6460,224,36, 14.09K,976,144, 25.18K,3064,1252, 30.89K,4848,2292, 32.46K,5156,2756, 33.68K,5468,3196, 35.16K,6028,3696, 37.05K,6276,4256, 38.36K,7016,5152, 39.3K,7644,5980, 40.3K,7772,6752, 1320,24,4, 5980,244,20, 12.68K,980,144, 22.86K,2740,944, 28.25K,4320,2108, 29.6K,4820,2540, 30.36K,4916,2736, 31.48K,5360,3220, 33.4K,5916,3808, 34.7K,6400,4700, 36.11K,7008,5428, 37.52K,7360,6108, 608,20,0, 3616,112,12, 8600,400,64, 18.09K,1908,488, 21.6K,3404,1184, 22.52K,3268,1500, 23.94K,3840,1832, 23.53K,3896,1896, 24.75K,4400,2612, 25.84K,4764,3136, 27.17K,5340,3820, 28.12K,5456,4164, 72,4,0, 596,0,4, 1732,84,4, 4040,392,56, 5404,628,184, 5724,748,264, 6288,804,352, 6604,860,428, 7528,976,484, 7532,1208,660, 8164,1308,752, 8392,1368,720, 44,0,0, 264,20,0, 584,52,8, 1420,180,40, 2004,204,60, 2460,268,96, 2708,300,120, 2572,256,156, 2968,364,180, 3296,340,216, 3704,408,296, 3832,536,340, 0,0,0, 0,0,0, 0,0,0, 12,0,0, 48,0,0, 40,0,0, 56,0,0, 80,4,0, 80,0,0, 88,8,0, 96,0,0, 140,8,0, 0,0,0, 2376,32,8, 12.17K,464,48, 29.4K,1764,172, 61.84K,6372,1860, 79.31K,10.48K,4264, 83.46K,11.78K,5048, 86.79K,12.8K,5768, 89.61K,13.58K,6728, 95.34K,15.5K,8888, 99.36K,17.46K,10.25K, 104K,19.26K,12.93K, 107.3K,20.12K,14.37K, 1820,28,0, 8580,372,20, 20.58K,1104,240, 42.14K,4724,1496, 52.54K,7192,3104, 53.98K,8136,3648, 57.07K,8860,4236, 58.45K,9428,4976, 60.42K,10.32K,5948, 63.16K,11.69K,7056, 66.02K,12.6K,8852, 67K,13.28K,9692, 1560,56,0, 7688,324,8, 18.36K,1172,144, 37.27K,4240,1268, 47.07K,6812,2984, 49.34K,7072,3500, 50.69K,7768,3816, 52.17K,8248,4648, 54.96K,9400,5708, 57.68K,10.45K,6484, 60.26K,11.34K,7908, 61.2K,11.76K,8776, 964,16,4, 5008,228,12, 11.86K,644,148, 23.2K,2648,748, 28.24K,4436,1760, 30.64K,4860,2092, 29.9K,5064,2216, 32.46K,5272,2768, 33.74K,6112,3512, 34.58K,6488,3972, 35.43K,7628,5136, 36.42K,7812,5744, 60,4,0, 320,12,0, 1048,64,0, 2432,240,52, 3180,360,100, 3248,408,208, 3460,456,212, 3464,536,204, 3580,656,264, 3744,668,384, 3856,804,404, 3940,856,540, 16,0,0, 144,0,0, 372,4,16, 784,68,12, 952,120,24, 920,124,52, 932,192,56, 964,196,80, 980,176,88, 932,220,120, 888,204,148, 924,240,176, 0,0,0, 24,0,0, 140,4,0, 312,24,0, 304,48,12, 360,52,20, 332,52,24, 288,56,8, 312,80,20, 348,72,24, 360,96,36, 360,108,56, 0,0,0, 3093,319,233, 5246,753,467, 6767,1126,862, 8826,1859,1674, 9674,2103,2124, 10.01K,2243,2219, 10.2K,2235,2407, 10.23K,2251,2450, 10.69K,2494,2662, 10.76K,2445,2773, 11.32K,2574,2822, 11.31K,2608,3004, 3030,332,185, 5118,714,467, 6692,1142,805, 8465,1774,1537, 9323,2142,2043, 9627,2145,2210, 9675,2131,2258, 9821,2285,2320, 10.24K,2294,2579, 10.41K,2446,2749, 10.85K,2513,2874, 10.87K,2519,2989, 3036,346,204, 5107,682,431, 6666,1038,817, 8723,1728,1558, 9255,2029,2058, 9515,2137,2202, 9716,2112,2271, 9950,2254,2361, 10.3K,2292,2587, 10.36K,2362,2802, 10.85K,2417,2975, 10.88K,2640,3012, 3002,394,229, 5189,717,481, 6722,1142,904, 8159,1674,1583, 8570,2042,2155, 8450,2055,2165, 8616,2107,2272, 8708,2253,2477, 8759,2247,2570, 8669,2365,2775, 8885,2337,2968, 8904,2327,2984, 1985,249,116, 3363,477,282, 4297,735,566, 5287,1142,1152, 5344,1312,1410, 5714,1307,1494, 5568,1389,1615, 5654,1437,1636, 5684,1538,1733, 5880,1536,1785, 5892,1500,1817, 6017,1488,1911, 1396,118,80, 2500,325,189, 3513,527,347, 4406,888,782, 4676,1097,1048, 4842,1066,1055, 4870,1192,1212, 5041,1137,1242, 5011,1204,1321, 5069,1241,1490, 5082,1313,1488, 5086,1283,1590, 575,50,36, 1186,111,73, 1858,222,134, 2717,437,344, 3068,583,411, 3152,593,522, 3159,685,549, 3306,642,597, 3371,700,694, 3348,735,731, 3512,804,780, 3527,852,832, 0,0,0, 293,27,20, 603,77,38, 1117,130,84, 1760,249,184, 2097,354,256, 2223,369,295, 2338,386,311, 2338,394,358, 2581,416,383, 2622,510,401, 2730,534,452, 2806,524,490, 202,16,14, 425,52,26, 667,77,56, 905,145,134, 1046,226,192, 1054,219,172, 1059,228,202, 1105,244,225, 1106,215,248, 1177,251,306, 1169,283,300, 1158,275,329, 181,14,13, 338,37,23, 533,64,43, 668,131,100, 700,167,163, 690,160,150, 748,179,178, 741,166,165, 807,191,191, 804,196,201, 861,197,218, 878,195,235, 301,30,19, 548,75,41, 692,112,88, 779,167,162, 830,190,216, 771,214,198, 805,213,250, 784,216,264, 781,207,292, 835,215,295, 802,227,303, 833,241,296, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 2140,220,115, 4107,516,300, 6049,841,571, 8764,1499,1178, 10.07K,1955,1581, 10.37K,1993,1685, 10.52K,2118,1904, 10.73K,2197,2012, 11.2K,2383,2192, 11.29K,2476,2344, 11.51K,2495,2535, 11.69K,2623,2743, 1930,199,108, 3671,412,259, 5470,744,484, 7953,1371,1052, 9014,1786,1415, 9266,1720,1589, 9503,1925,1668, 9684,1952,1729, 10.03K,2106,2049, 10.18K,2231,2150, 10.44K,2378,2295, 10.6K,2436,2587, 1896,187,91, 3421,437,205, 5205,787,472, 7697,1334,992, 8787,1698,1361, 8976,1795,1488, 9516,1809,1615, 9386,1887,1670, 9802,2025,1894, 9834,2149,2017, 10.25K,2347,2152, 10.18K,2384,2350, 1274,167,66, 2234,322,171, 3371,534,370, 4731,856,714, 5374,1063,978, 5685,1121,1013, 5852,1170,1057, 5823,1194,1144, 6080,1234,1263, 6243,1340,1354, 6406,1398,1449, 6427,1487,1452, 879,96,47, 1721,201,109, 2682,356,236, 4081,716,519, 4672,877,699, 4922,924,752, 4960,952,873, 4968,989,867, 5346,1068,982, 5297,1127,1046, 5410,1151,1159, 5694,1252,1219, 574,60,24, 1169,143,47, 1797,240,122, 2909,480,337, 3515,581,475, 3749,620,489, 3862,705,527, 3941,716,583, 4036,781,634, 4278,788,728, 4316,816,820, 4405,882,879, 339,35,20, 757,76,41, 1290,136,88, 2231,311,204, 2804,465,317, 2889,469,315, 3006,472,370, 3080,546,398, 3194,533,442, 3255,609,498, 3432,672,570, 3459,744,587, 0,0,0, 2352,261,109, 4048,579,336, 5260,924,656, 6465,1411,1294, 7279,1549,1636, 7419,1641,1765, 7322,1699,1847, 7591,1754,1891, 7691,1802,2038, 7642,1877,2116, 7974,1928,2207, 7908,1922,2322, 1097,115,51, 2034,281,168, 2873,457,266, 3737,756,596, 4070,922,808, 4274,893,881, 4256,995,970, 4289,983,995, 4409,1028,1104, 4420,1107,1162, 4562,1124,1265, 4666,1096,1278, 828,88,42, 1590,206,111, 2292,343,246, 3215,621,442, 3395,747,637, 3620,782,685, 3626,800,709, 3598,807,776, 3832,885,870, 3894,869,927, 3908,981,954, 3879,965,988, 95,11,8, 220,27,24, 279,47,16, 277,46,41, 299,58,54, 493,88,83, 278,67,71, 499,117,98, 532,125,118, 549,101,111, 541,152,127, 502,137,130, 76,9,4, 150,19,12, 210,40,13, 297,56,47, 305,66,51, 319,66,71, 305,72,75, 279,72,64, 301,63,66, 343,66,85, 327,90,88, 342,72,94, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 10.06K,1101,581, 18.4K,2280,1394, 25.38K,3982,2671, 33.38K,6473,5510, 36.68K,7629,7400, 37.36K,7941,7706, 38.22K,8300,8121, 38.57K,8471,8679, 39.61K,8917,9322, 40.19K,9270,9847, 40.83K,9744,10.76K, 40.94K,10.15K,11.26K, 8727,1001,488, 16.56K,2053,1251, 23.11K,3540,2358, 30.91K,5937,4929, 34.51K,7080,6647, 34.96K,7271,7092, 35.26K,7749,7409, 35.91K,7886,7920, 36.7K,8480,8627, 37.18K,8547,9147, 38.11K,8949,9866, 38.38K,9128,10.36K, 7911,886,431, 14.8K,1815,1164, 20.41K,3096,2129, 27.76K,5183,4450, 30.98K,6525,5927, 31.94K,6740,6510, 32.08K,6990,6771, 32.84K,7162,7134, 33.28K,7542,7838, 33.78K,7603,8145, 34.57K,8137,8999, 35.08K,8382,9366, 5763,599,315, 10.8K,1391,739, 15.46K,2376,1466, 21.39K,3997,3189, 23.93K,4853,4616, 23.98K,5041,4790, 24.94K,5344,5128, 25.13K,5395,5356, 25.36K,5608,5795, 26.26K,5930,6032, 26.63K,6146,6575, 26.95K,6463,6948, 1367,158,72, 2798,326,203, 4308,576,361, 6764,1037,751, 7935,1389,1080, 8123,1459,1236, 8521,1532,1278, 8540,1649,1308, 8692,1742,1567, 8966,1914,1740, 9236,2027,1882, 9362,2063,2027, 591,52,33, 1252,125,64, 1974,236,142, 3338,478,317, 4117,643,474, 4224,691,526, 4340,703,562, 4486,721,610, 4641,820,708, 4728,886,737, 4948,1005,883, 5033,1052,902, 3,1,0, 19,1,0, 38,4,4, 75,10,6, 98,16,6, 91,12,10, 98,22,12, 101,14,11, 126,24,5, 114,17,12, 123,31,17, 106,17,17, 0,0,0, 19.39K,2009,1013, 37.77K,4637,2500, 54.57K,7870,5087, 76.27K,13.73K,10.68K, 85.79K,17.37K,14.98K, 87.58K,17.85K,16.02K, 89.43K,18.93K,17.34K, 90.99K,19.31K,18.34K, 93.03K,20.56K,20.05K, 95.98K,21.4K,21.66K, 97.43K,22.53K,23.76K, 99K,23.02K,24.85K, 13.02K,1428,734, 24.87K,3242,1851, 36.04K,5452,3390, 48.75K,9269,7293, 54.04K,11.13K,10.14K, 54.95K,11.84K,10.92K, 55.92K,12.32K,11.41K, 56.7K,12.75K,12.14K, 57.81K,13.4K,13.47K, 58.89K,13.59K,14.48K, 59.99K,14.46K,15.7K, 61.64K,14.69K,16.29K, 11.93K,1282,610, 22.71K,2815,1618, 32.05K,4895,3068, 44.22K,8141,6540, 48.73K,10.03K,9032, 49.67K,10.6K,9496, 50.66K,10.87K,10.31K, 51.35K,11.47K,10.93K, 52.82K,11.88K,11.95K, 53.52K,12.37K,12.87K, 54.58K,12.92K,14K, 55.52K,13.29K,14.72K, 7083,742,334, 14.04K,1801,1066, 20.11K,3006,1983, 24.72K,4909,3928, 26.75K,5927,5606, 28.77K,6463,6167, 27.3K,6310,6318, 29.77K,6859,6923, 30.09K,7283,7489, 30.72K,7580,8108, 31.12K,7723,8758, 31.05K,7843,9000, 706,70,33, 1388,166,118, 2108,299,188, 2947,560,408, 3014,629,600, 3056,688,596, 3181,725,699, 3089,764,740, 3217,794,780, 3232,835,804, 3230,841,920, 3157,804,1018, 228,21,13, 441,60,28, 680,79,67, 872,188,142, 907,229,177, 916,231,193, 867,244,228, 944,219,228, 941,243,283, 976,230,258, 984,251,266, 982,271,314, 74,10,1, 197,20,10, 277,34,20, 350,79,52, 435,76,78, 462,99,87, 421,98,73, 426,98,98, 457,99,117, 454,106,119, 513,117,120, 453,99,135, 52.13K,4594,2495, 50.99K,4382,2479, 48.99K,4319,2448, 46.35K,4028,2307, 38.87K,3347,1957, 31.45K,2714,1561, 28.84K,2455,1399, 25.89K,2253,1216, 23.49K,1939,1127, 17.99K,1601,899, 13.03K,1142,601, 4953,444,254, 0,0,0, 50.7K,4382,2435, 49.08K,4321,2408, 46.76K,4068,2244, 39.18K,3466,1997, 31.6K,2714,1574, 29.08K,2513,1406, 26.23K,2394,1269, 24.01K,2081,1215, 18.72K,1614,899, 13.55K,1240,632, 6299,549,293, 1316,125,66, 50.73K,4488,2580, 49.58K,4362,2357, 46.86K,4065,2315, 39.52K,3511,2004, 32.12K,2863,1491, 29.64K,2735,1434, 27.38K,2431,1336, 24.94K,2218,1218, 20.36K,1788,978, 15.58K,1309,779, 8306,663,393, 3465,317,162, 50.66K,4469,2417, 49.64K,4266,2443, 47.54K,4124,2307, 41.3K,3628,2084, 34.4K,3091,1696, 32.52K,2850,1600, 30.44K,2750,1446, 28.43K,2514,1380, 24.16K,2153,1157, 19.6K,1665,974, 13.18K,1152,676, 9008,795,431, 50.88K,4409,2508, 49.87K,4357,2494, 48.3K,4244,2343, 43.86K,3912,2173, 39.07K,3453,1962, 37.43K,3385,1813, 35.48K,3218,1770, 34.39K,3011,1656, 30.54K,2696,1466, 27.38K,2439,1332, 22.82K,1950,1109, 19.47K,1754,941, 51.27K,4496,2534, 50.73K,4400,2388, 49.2K,4363,2434, 45.95K,4114,2193, 41.92K,3590,2047, 40.83K,3603,1984, 39.73K,3381,1919, 38.26K,3384,1876, 36.21K,3161,1722, 33.52K,2990,1712, 29.58K,2632,1487, 27.26K,2500,1316, 51.26K,4413,2564, 50.7K,4378,2388, 50.19K,4411,2406, 48.11K,4180,2294, 46.12K,3932,2218, 44.99K,4073,2200, 44.11K,3845,2190, 43.21K,3814,2155, 41.66K,3710,1973, 40.54K,3518,1983, 37.8K,3369,1811, 36.71K,3197,1747, 13.95K,1247,703, 13.68K,1194,652, 13.33K,1177,696, 12.73K,1103,635, 10.42K,987,493, 8378,721,412, 7821,688,372, 7052,604,324, 6355,556,295, 4810,448,237, 3499,322,193, 1398,144,67, 0,0,0, 13.93K,1200,684, 13.42K,1215,648, 12.81K,1140,660, 11.24K,989,552, 9625,848,459, 9185,783,467, 8649,766,421, 8118,657,421, 7031,606,328, 6174,504,312, 4355,361,240, 3319,289,148, 13.8K,1223,661, 13.48K,1128,673, 12.91K,1139,639, 11.48K,1017,540, 9780,847,473, 9447,805,459, 8936,818,440, 8544,676,423, 7254,639,367, 6522,534,322, 4913,448,257, 3773,338,185, 13.89K,1193,682, 13.62K,1161,707, 13.06K,1065,626, 11.8K,1000,574, 10.26K,917,493, 9744,866,426, 9425,841,453, 8758,746,410, 7853,676,401, 7044,645,335, 5723,486,271, 4586,393,220, 14.08K,1207,722, 13.96K,1217,679, 13.85K,1224,707, 13.96K,1144,669, 13.85K,1233,664, 13.97K,1247,683, 14.19K,1236,726, 14.03K,1224,666, 13.82K,1278,666, 13.76K,1241,643, 13.75K,1259,660, 13.72K,1169,715, 13.99K,1263,701, 14.02K,1192,705, 13.93K,1214,738, 14.09K,1173,682, 14.07K,1255,700, 13.97K,1280,680, 13.93K,1236,670, 13.78K,1218,676, 13.89K,1211,697, 14K,1165,691, 13.78K,1250,662, 14.17K,1261,707, 14.14K,1210,649, 13.92K,1269,722, 13.85K,1227,671, 14.09K,1267,719, 14K,1299,717, 14.01K,1232,689, 13.93K,1244,661, 14.01K,1251,665, 13.85K,1196,715, 13.95K,1214,649, 13.91K,1267,666, 13.92K,1246,709, 72.53K,6427,3587, 71.55K,6161,3591, 68.99K,6109,3392, 66.33K,5696,3153, 54.48K,4781,2609, 43.74K,3887,2154, 40.09K,3514,1952, 36.61K,3190,1847, 32.65K,2887,1620, 25.42K,2249,1275, 18.23K,1525,926, 7375,626,336, 0,0,0, 71.62K,6390,3588, 69.49K,6006,3494, 65.3K,5779,3215, 55.1K,4902,2639, 44.31K,3881,2121, 40.92K,3645,2003, 37.37K,3255,1841, 34.25K,3000,1726, 26.73K,2343,1325, 19.73K,1795,946, 8943,792,446, 2086,169,87, 71.92K,6229,3432, 69.7K,6166,3376, 66.26K,5890,3231, 56.38K,4906,2715, 46.24K,4179,2285, 43.27K,3756,2213, 39.77K,3560,1886, 36.59K,3314,1805, 30.17K,2650,1481, 23.1K,2042,1110, 13.43K,1167,673, 6802,653,315, 72.24K,6458,3496, 70.48K,6234,3388, 66.95K,5891,3317, 59.7K,5195,3030, 51.83K,4541,2520, 48.76K,4313,2430, 46.48K,4082,2246, 43.99K,3770,2147, 38.61K,3479,1954, 33.29K,2925,1637, 25.21K,2184,1252, 19.81K,1759,1019, 72.01K,6491,3501, 71.35K,6186,3515, 68.85K,5925,3427, 63.56K,5560,3059, 57.38K,5002,2879, 55.56K,4879,2702, 53.61K,4913,2574, 51.11K,4547,2551, 47.88K,4106,2316, 44.06K,3846,2202, 37.45K,3242,1849, 33.77K,3072,1648, 72.85K,6408,3577, 71.7K,6225,3595, 70.53K,6272,3429, 67.16K,5990,3307, 63.11K,5624,3093, 62.91K,5487,3100, 61.26K,5397,2863, 60.96K,5268,2833, 57.96K,5102,2821, 55.94K,5025,2573, 52.55K,4523,2524, 49.88K,4538,2502, 72.3K,6203,3632, 72.29K,6229,3562, 71.45K,6372,3452, 69.87K,6142,3375, 67.84K,6046,3370, 67.15K,5831,3288, 66.6K,5922,3147, 66.01K,5846,3305, 64.74K,5736,3111, 63.68K,5513,3114, 62.24K,5442,3018, 60.4K,5398,2954, 124.2K,10.92K,6033, 121.7K,10.61K,5990, 117.8K,10.46K,5630, 111.5K,9836,5407, 92.49K,8201,4630, 74.14K,6481,3573, 68.4K,5916,3352, 62.43K,5403,3097, 55.76K,4948,2672, 43.73K,3841,2179, 31.36K,2744,1513, 12.37K,1060,614, 0,0,0, 123.1K,10.85K,6141, 120K,10.49K,5768, 115.8K,10.26K,5697, 102.4K,9071,5068, 89.2K,7967,4344, 84.66K,7444,4229, 80.09K,7163,3915, 76.15K,6603,3705, 67.33K,5975,3225, 58.24K,5021,2832, 45.31K,3985,2337, 36.14K,3233,1881, 122.5K,10.69K,6089, 121K,10.63K,6020, 117.2K,10.15K,5706, 106.7K,9294,5084, 96.86K,8582,4755, 93.5K,8174,4594, 89.09K,7941,4367, 86.35K,7665,4122, 80.21K,7084,3750, 72.2K,6327,3499, 61.96K,5397,3101, 55.61K,4713,2721, 123.3K,10.85K,6026, 122.7K,10.77K,5970, 122.2K,10.73K,5898, 117.9K,10.52K,5751, 113.4K,9970,5545, 112.9K,10.04K,5512, 111.5K,9766,5515, 110.2K,9737,5457, 108K,9561,5243, 105K,9471,5197, 102K,8979,4978, 99.87K,8766,4855, 124.3K,11.01K,6071, 123.3K,10.85K,5991, 123.3K,10.83K,5882, 122.1K,10.69K,5934, 121.6K,10.79K,5968, 120.8K,10.48K,6022, 121.5K,10.62K,5996, 120.6K,10.59K,5993, 120.3K,10.45K,5887, 119.6K,10.39K,5798, 118.5K,10.32K,5665, 117.8K,10.42K,5759, 123.7K,11.09K,6191, 123.7K,10.79K,6097, 123.7K,10.93K,6082, 124.3K,11K,6031, 124K,10.73K,5949, 123.3K,10.71K,6208, 123.5K,10.74K,6074, 123.6K,10.84K,6054, 122.8K,10.93K,6005, 123K,10.82K,6038, 123.5K,10.8K,5972, 123.7K,10.73K,6022, 123.7K,10.96K,6208, 124.7K,10.83K,6101, 124.2K,10.97K,6024, 124.2K,10.74K,5977, 124.4K,10.84K,6065, 124.2K,10.8K,5988, 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Each row contains the values for the input variables used for the scenario. Table(Input_var,Scenario102)( 0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5, 7,6,5,4,3,2,1,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, 'Yes','Yes','Yes','Yes','Yes','Yes','Yes',0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0.2,0.4,0.6,0.8,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.2,0.2,0.2,0.2,0.2,0.4,0.4,0.4,0.4,0.4,0.6,0.6,0.6,0.6,0.6,0.8,0.8,0.8,0.8,0.8,1,1,1,1, 0,0,0,0,0,0,0,0,0,0,0,0.2,0.4,0.6,0.8,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.2,0.4,0.6,0.8,1,0.2,0.4,0.6,0.8,1,0.2,0.4,0.6,0.8,1,0.2,0.4,0.6,0.8,1,0.2,0.4,0.6,0.8 ) ['Composite fraction','Guarantee level','Lim'] 176,152,1 48,24 2,394,67,698,560,0,MIDM 2,327,20,829,669,0,MIDM 52425,39321,65535 Graphtool:0 Distresol:10 Diststeps:1 Cdfresol:5 Cdfsteps:1 Symbolsize:6 Baroverlap:0 Linestyle:1 Frame:1 Grid:1 Ticks:1 Mesh:1 Scales:1 Rotation:45 Tilt:0 Depth:70 Frameauto:1 Showkey:1 Xminimum:0 Xmaximum:1 Yminimum:0 Ymaximum:1 Zminimum:0 Zmaximum:1 Xintervals:0 Yintervals:0 Includexzero:0 Includeyzero:0 Includezzero:0 Statsselect:[1,1,1,1,1,0,0,0] Probindex:[0.05,0.25,0.5,0.75,0.95] [Input_var,Scenario] [Input_var,Scenario102] [0,0,1,0] Index for a list of scenarios to be modelled. [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78] 176,184,1 48,12 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78] Table(Length_1,Zone,Vehicle_noch,Output_1,Scenario102,Period_1)( 26.13K,2297,1254, 25.98K,2212,1237, 25.6K,2327,1304, 25.86K,2199,1245, 25.56K,2345,1313, 25.54K,2336,1245, 25.78K,2282,1256, 25.96K,2273,1303, 26.07K,2174,1252, 25.64K,2264,1281, 26K,2273,1237, 25.59K,2271,1272, 25.87K,2170,1245, 26.06K,2201,1238, 26.09K,2247,1236, 0,0,0, 990,95,56, 2581,252,120, 5219,454,237, 12.97K,1168,620, 20.74K,1776,1055, 23.04K,2014,1077, 25.37K,2300,1201, 28.35K,2441,1368, 33.8K,2856,1662, 38.65K,3474,1981, 46.76K,4127,2212, 51.81K,4461,2540, 0,0,0, 1096,89,51, 2603,226,125, 5121,453,253, 12.75K,1104,613, 20.77K,1829,1026, 23.13K,2100,1176, 26.15K,2281,1246, 28.51K,2423,1417, 33.71K,2965,1613, 38.66K,3422,1874, 46.71K,4016,2275, 51.99K,4508,2579, 0,0,0, 1028,89,49, 2502,221,100, 5246,506,235, 13.18K,1137,626, 20.6K,1767,1032, 23.21K,2066,1113, 25.38K,2282,1266, 28.46K,2439,1414, 33.4K,3007,1692, 38.93K,3367,1855, 46.64K,4066,2364, 51.78K,4616,2514, 25.9K,2249,1305, 25.72K,2311,1194, 26.05K,2269,1259, 25.63K,2268,1204, 25.65K,2290,1263, 25.9K,2238,1247, 25.83K,2246,1235, 25.92K,2221,1217, 25.84K,2261,1237, 25.64K,2302,1330, 25.93K,2339,1298, 25.69K,2230,1245, 26.18K,2285,1245, 25.87K,2273,1223, 25.81K,2302,1216, 26.23K,2233,1224, 25.93K,2284,1337, 25.59K,2227,1256, 25.72K,2311,1231, 25.79K,2248,1339, 25.68K,2210,1229, 25.77K,2210,1242, 26.18K,2258,1247, 26.07K,2243,1214, 2888,16,0, 2928,8,0, 2656,40,0, 3080,8,0, 2760,24,8, 2784,0,0, 3016,16,0, 2976,32,0, 3288,8,0, 2920,16,0, 2688,16,0, 2952,64,0, 3680,48,0, 4584,96,0, 5752,72,0, 0,0,0, 0,0,0, 0,0,0, 8,0,0, 304,0,0, 1688,8,0, 2264,8,0, 2776,24,0, 3832,32,0, 5520,64,0, 7728,80,8, 11.1K,184,0, 13.86K,264,16, 0,0,0, 0,0,0, 8,0,0, 56,0,0, 1448,0,0, 4632,40,0, 5440,128,0, 7240,96,8, 8344,128,16, 11.34K,256,24, 14.86K,352,40, 20.41K,616,72, 24.38K,640,64, 0,0,0, 0,0,0, 0,0,0, 80,0,0, 1648,0,8, 4592,56,0, 5544,64,0, 6752,144,8, 8528,128,16, 11.18K,296,16, 15.06K,352,8, 20.46K,480,64, 24.22K,840,40, 3096,16,0, 3544,64,0, 4432,80,0, 5544,96,0, 6720,88,8, 3224,16,0, 3672,40,0, 4616,56,0, 5632,96,8, 7088,104,32, 3008,24,0, 3392,24,0, 4552,48,8, 5728,56,0, 6960,208,8, 3392,56,0, 3784,48,0, 4512,40,0, 5424,72,0, 7168,144,8, 3168,0,0, 3616,48,0, 4792,80,0, 5624,80,8, 3771,35.9,6, 3824,23,3, 3579,30.6,0, 2900,32,0, 1567,7.1,3.3, 1678,0,0, 1437,5.3,0, 3813,33.3,3, 3998,31.4,3, 3886,24.2,0, 3680,23.4,3, 4102,51.8,0, 5119,55.6,0, 6674,93.5,6, 8578,123.6,3, 0,0,0, 0,0,0, 0,0,0, 17,0,0, 449.8,0,0, 2001,8,0, 2870,17,3, 3813,25.3,3, 4933,30.5,3, 7778,84.4,6, 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10.5K,2127,2081, 8854,1826,1923, 12.8K,2456,2425, 12.63K,2357,2361, 12.22K,2347,2303, 12.06K,2264,2121, 10.09K,1970,1970, 16.11K,2293,1499, 15.71K,2258,1468, 15.16K,2182,1433, 13.81K,2066,1445, 11.97K,2013,1402, 16.66K,2035,1222, 16.23K,2037,1311, 15.21K,1969,1252, 14.06K,1935,1220, 12.03K,1792,1294, 16.66K,1990,1209, 15.98K,1938,1214, 15.15K,1926,1195, 13.9K,1803,1134, 98.83K,23.17K,24.65K, 96.57K,22.62K,24.44K, 90.75K,21.45K,22.61K, 82.61K,19.04K,21.05K, 58.66K,13.8K,15.45K, 44.41K,10.4K,11.09K, 29.45K,6797,7433, 116.5K,23.73K,24.63K, 157.5K,27.46K,25.88K, 247.5K,35.4K,22.6K, 233.8K,31.16K,20.12K, 98.37K,22.62K,24.71K, 97.54K,22.47K,24.49K, 95.91K,21.88K,23.74K, 91.83K,20.42K,22.76K, 0,0,0, 17.73K,1595,908.5, 41.73K,3929,2161, 75.09K,7400,4214, 149.3K,16.96K,10.5K, 198.9K,25.09K,16.27K, 211.8K,27.41K,17.88K, 223.2K,29.89K,19.89K, 236.2K,31.85K,21.7K, 253K,35.7K,25.02K, 269.6K,40.18K,28.07K, 289.4K,44.8K,33.07K, 301.4K,47.95K,35.82K, 0,0,0, 28.45K,3831,2599, 43.52K,6860,5628, 55.38K,10.15K,8920, 70.92K,14.59K,14.93K, 76.94K,17.17K,17.88K, 77.39K,17.75K,19.43K, 78.54K,18.06K,20.09K, 79.72K,18.78K,20.54K, 81.23K,19.32K,22.03K, 81.54K,19.81K,23.03K, 83K,20.68K,24.71K, 83.97K,20.87K,24.72K, 0,0,0, 16.78K,1553,905.5, 37.94K,3832,2173, 65.04K,7145,3954, 112.2K,15K,9984, 138.7K,20.82K,15.45K, 145.4K,22.5K,16.52K, 150.6K,24.89K,18.22K, 154.9K,25.89K,19.43K, 163.7K,28.13K,22.3K, 169.3K,30.73K,24.56K, 176.4K,33.29K,28.42K, 181.1K,35.35K,30.18K, 116.5K,23.75K,24.92K, 115.3K,23.58K,24.76K, 113.3K,22.6K,23.76K, 109.8K,21.71K,22.58K, 97.38K,19.53K,20.61K, 157.7K,27.38K,25.98K, 154.5K,26.65K,26.06K, 150K,26.23K,25.01K, 145.1K,25.46K,24.3K, 131.2K,23.61K,22.32K, 245.5K,34.63K,22.9K, 238.5K,34.33K,22.41K, 228.3K,33.57K,22.26K, 209.3K,31.85K,22.02K, 181K,30.25K,21.96K, 233.4K,31.1K,19.72K, 226K,30.73K,19.56K, 215.1K,29.33K,19.81K, 197K,28.28K,19.38K, 167.6K,26.66K,19.08K, 222.3K,29.66K,20.26K, 216.3K,28.77K,19.58K, 203.1K,27.68K,19.41K, 182K,26.55K,18.49K, 0,0,0, 0,0,0, 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1324,0,0, 1418,0,0, 1488,0,0, 1553,0,0, 1659,0,0, 1803,0,0, 1911,0,0, 2001,0,0, 331.5,0,0, 343.9,0,0, 338.5,0,0, 333,0,0, 314.1,0,0, 709.1,0,0, 705.8,0,0, 699,0,0, 695.8,0,0, 682,0,0, 1848,0,0, 1832,0,0, 1800,0,0, 1676,0,0, 1555,0,0, 1814,0,0, 1811,0,0, 1759,0,0, 1671,0,0, 1535,0,0, 1785,0,0, 1768,0,0, 1712,0,0, 1625,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 6.417,8.558,10.53, 6.397,8.487,10.57, 6.385,8.474,10.57, 6.329,8.595,10.57, 6.047,8.18,10.25, 5.909,7.982,10.07, 5.859,7.76,9.808, 6.86,8.696,10.59, 7.885,9.281,10.82, 10.38,11.57,11.94, 11,11.84,11.99, 6.457,8.592,10.54, 6.476,8.57,10.41, 6.567,8.572,10.27, 6.664,8.518,10.2, 'NAN','NAN','NAN', 12,12,12, 12,12,12, 11.96,12,12, 11.77,11.95,12, 11.5,11.94,11.99, 11.4,11.91,11.99, 11.3,11.89,11.99, 11.21,11.87,11.98, 10.99,11.84,11.98, 10.82,11.75,11.97, 10.54,11.7,11.95, 10.35,11.66,11.94, 'NAN','NAN','NAN', 11.23,11.89,11.97, 10.1,11.47,11.92, 8.881,10.77,11.63, 7.433,9.332,10.83, 6.798,8.545,10.13, 6.667,8.339,9.94, 6.576,8.18,9.838, 6.484,8.016,9.632, 6.331,7.774,9.365, 6.207,7.54,9.131, 6.06,7.289,8.829, 6,7.138,8.595, 'NAN','NAN','NAN', 12,12,12, 11.97,12,12, 11.85,12,12, 11.3,11.87,11.99, 10.71,11.71,11.97, 10.53,11.68,11.95, 10.37,11.64,11.94, 10.18,11.62,11.92, 9.873,11.48,11.9, 9.52,11.36,11.87, 9.1,11.13,11.81, 8.86,11.05,11.78, 6.879,8.729,10.59, 6.931,8.762,10.44, 6.977,8.749,10.36, 7.099,8.695,10.22, 7.064,8.441,9.882, 7.882,9.338,10.82, 7.895,9.327,10.73, 7.925,9.339,10.63, 7.959,9.305,10.52, 7.919,9.152,10.25, 10.35,11.53,11.93, 10.27,11.49,11.92, 10.19,11.43,11.87, 9.995,11.33,11.85, 9.596,11.19,11.81, 10.98,11.83,11.98, 10.91,11.78,11.98, 10.78,11.72,11.97, 10.54,11.68,11.96, 10.14,11.58,11.93, 11.27,11.88,11.99, 11.2,11.86,11.98, 11.04,11.81,11.96, 10.79,11.73,11.96, 26K,2297,1241, 26.04K,2255,1292, 25.91K,2218,1274, 25.69K,2283,1297, 25.99K,2198,1296, 26.22K,2248,1314, 26.17K,2234,1262, 25.89K,2253,1216, 25.63K,2315,1284, 25.61K,2322,1240, 25.89K,2233,1213, 25.76K,2395,1261, 25.76K,2238,1293, 25.68K,2232,1272, 25.79K,2295,1281, 51.76K,4551,2510, 50.79K,4438,2534, 49.24K,4264,2411, 46.4K,4077,2279, 38.43K,3488,1863, 30.97K,2772,1499, 28.88K,2484,1421, 26.05K,2285,1208, 23.36K,2001,1096, 18.01K,1649,889, 13.08K,1096,658, 5271,448,261, 0,0,0, 51.54K,4541,2592, 50.62K,4556,2393, 48.99K,4433,2403, 46.43K,4130,2253, 38.82K,3430,1857, 31.27K,2710,1486, 28.48K,2515,1388, 25.45K,2245,1255, 23.12K,2004,1127, 17.91K,1538,881, 13.12K,1109,612, 5150,470,261, 0,0,0, 51.76K,4624,2607, 50.33K,4511,2466, 49.32K,4431,2373, 46.5K,4101,2286, 38.48K,3408,1890, 30.89K,2650,1537, 28.39K,2578,1463, 25.82K,2170,1229, 23.34K,2024,1136, 18.19K,1566,889, 13.04K,1092,645, 5127,430,281, 0,0,0, 25.9K,2374,1250, 25.74K,2315,1267, 25.87K,2291,1265, 25.94K,2249,1308, 25.85K,2266,1280, 25.6K,2220,1213, 25.68K,2316,1244, 25.7K,2284,1326, 25.9K,2263,1236, 25.77K,2185,1260, 26.17K,2319,1233, 25.83K,2224,1312, 25.91K,2275,1229, 25.82K,2233,1253, 25.84K,2279,1285, 25.95K,2280,1247, 26.23K,2285,1210, 25.92K,2279,1320, 25.7K,2340,1339, 25.85K,2225,1223, 25.86K,2175,1253, 26.02K,2320,1222, 25.96K,2246,1236, 25.99K,2238,1252, 26K,2297,1241, 26.04K,2255,1292, 25.91K,2218,1274, 25.69K,2283,1297, 25.99K,2198,1296, 26.22K,2248,1314, 26.17K,2234,1262, 25.89K,2253,1216, 25.63K,2315,1284, 25.61K,2322,1240, 25.89K,2233,1213, 25.76K,2395,1261, 25.76K,2238,1293, 25.68K,2232,1272, 25.79K,2295,1281, 51.76K,4551,2510, 50.79K,4438,2534, 49.24K,4264,2411, 46.4K,4077,2279, 38.43K,3488,1863, 30.97K,2772,1499, 28.88K,2484,1421, 26.05K,2285,1208, 23.36K,2001,1096, 18.01K,1649,889, 13.08K,1096,658, 5271,448,261, 0,0,0, 51.54K,4541,2592, 50.62K,4556,2393, 48.99K,4433,2403, 46.43K,4130,2253, 38.82K,3430,1857, 31.27K,2710,1486, 28.48K,2515,1388, 25.45K,2245,1255, 23.12K,2004,1127, 17.91K,1538,881, 13.12K,1109,612, 5150,470,261, 0,0,0, 51.76K,4624,2607, 50.33K,4511,2466, 49.32K,4431,2373, 46.5K,4101,2286, 38.48K,3408,1890, 30.89K,2650,1537, 28.39K,2578,1463, 25.82K,2170,1229, 23.34K,2024,1136, 18.19K,1566,889, 13.04K,1092,645, 5127,430,281, 0,0,0, 25.9K,2374,1250, 25.74K,2315,1267, 25.87K,2291,1265, 25.94K,2249,1308, 25.85K,2266,1280, 25.6K,2220,1213, 25.68K,2316,1244, 25.7K,2284,1326, 25.9K,2263,1236, 25.77K,2185,1260, 26.17K,2319,1233, 25.83K,2224,1312, 25.91K,2275,1229, 25.82K,2233,1253, 25.84K,2279,1285, 25.95K,2280,1247, 26.23K,2285,1210, 25.92K,2279,1320, 25.7K,2340,1339, 25.85K,2225,1223, 25.86K,2175,1253, 26.02K,2320,1222, 25.96K,2246,1236, 25.99K,2238,1252, 398K,38.34K,22.06K, 401.2K,38.68K,22.63K, 412.9K,39.61K,23.25K, 455.9K,43.41K,25.79K, 521.2K,51.1K,29.91K, 560K,54.74K,32.01K, 606.8K,59.68K,35.13K, 397.7K,38.36K,22.4K, 396.1K,38.17K,22.38K, 396.6K,39.2K,21.77K, 397.1K,38.44K,21.57K, 396.1K,38.69K,22.49K, 393.9K,38.36K,22.27K, 388.1K,37.64K,22.47K, 380.6K,37.45K,22.08K, 737.7K,72.25K,42.8K, 724.5K,71.58K,42.56K, 703.7K,69.23K,40.89K, 671.1K,66.43K,39.28K, 569.9K,55.89K,32.56K, 467.6K,45.57K,26.33K, 434.4K,41.47K,24.6K, 397.3K,38.63K,22.26K, 362.4K,34.59K,19.32K, 288.1K,27.31K,15.85K, 212.5K,19.75K,11.48K, 90.14K,7832,4621, 0,0,0, 688.9K,67.95K,41.7K, 677.8K,67.25K,40.13K, 659.3K,65.1K,39.66K, 626.1K,62.4K,37.6K, 530.9K,53.28K,31.77K, 435.5K,42.7K,25.76K, 402.3K,39.52K,23.77K, 369.4K,35.82K,21.71K, 336.5K,33.32K,19.97K, 267.7K,26.34K,15.65K, 199.7K,19.24K,10.97K, 85.34K,8087,4640, 0,0,0, 689.9K,68.6K,41.68K, 676.6K,66.68K,41.52K, 658.5K,65.7K,39.12K, 627.2K,61.73K,37.53K, 532.6K,52.44K,32.19K, 433.2K,43.04K,26.13K, 399.7K,39.83K,23.53K, 367.9K,36.6K,21.85K, 336.4K,33.07K,19.21K, 270.8K,25.83K,15.43K, 199.2K,19.01K,11.28K, 85.37K,7845,4432, 0,0,0, 396.5K,38.65K,22.3K, 394.2K,39.11K,22.31K, 388.4K,37.45K,21.96K, 381.6K,37.65K,21.6K, 368.4K,36.33K,21.67K, 396.3K,38.14K,21.74K, 390.9K,38.27K,21.87K, 389.8K,37.42K,22.1K, 380.9K,37.08K,21.91K, 367.1K,36.23K,21.66K, 395.4K,38.99K,22.02K, 392.2K,38.14K,21.93K, 389.3K,37.7K,21.8K, 380.2K,37.25K,22.04K, 369.2K,36.4K,21.7K, 396.6K,38.34K,23.01K, 394K,38.34K,21.73K, 389.1K,37.86K,21.99K, 380.2K,36.68K,22.06K, 368.4K,35.65K,21.58K, 397.6K,37.48K,22.06K, 392.7K,37.78K,21.73K, 388.7K,37.37K,21.8K, 382.6K,37.19K,22.27K, 3847,0,0, 4013,0,0, 3825,0,0, 3420,0,0, 3697,0,0, 3784,0,0, 3643,0,0, 3806,0,0, 4055,0,0, 3945,0,0, 3664,0,0, 3601,0,0, 3791,0,0, 3582,0,0, 3970,0,0, 6769,0,0, 7314,0,0, 6809,0,0, 6194,0,0, 5788,0,0, 4897,0,0, 4124,0,0, 3705,0,0, 3582,0,0, 2603,0,0, 2018,0,0, 1024,0,0, 0,0,0, 6369,0,0, 7078,0,0, 6325,0,0, 6037,0,0, 5112,0,0, 4157,0,0, 4230,0,0, 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289.2K,29.87K,17.31K, 309.7K,32.65K,19.1K, 350.1K,37.81K,22.59K, 386.4K,43.29K,25.57K, 435.9K,50.49K,30.58K, 464.2K,55.25K,33.79K, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 14.06K,1258,690, 35K,3076,1639, 66.75K,6305,3520, 144.4K,14.95K,8623, 201.4K,23.13K,13.57K, 217.9K,25.18K,15.37K, 233.4K,27.72K,16.96K, 246.7K,29.94K,18.57K, 271.2K,34.42K,21.53K, 293.9K,38K,24.82K, 322K,44.23K,29.58K, 338.4K,47.82K,32.17K, 17.21K,758,143, 17.05K,822,174, 17.09K,903,190, 17.83K,1053,235, 19.15K,1186,301, 61.64K,4066,1286, 60.17K,4039,1268, 58.84K,4300,1408, 57.13K,4610,1589, 58.26K,4892,1900, 225.4K,25.43K,16.04K, 219.9K,25.57K,16.02K, 210K,24.81K,15.82K, 195.3K,23.98K,15.46K, 174.5K,22.78K,15.2K, 269.6K,28.88K,17.06K, 263.1K,28.65K,17.19K, 251K,27.98K,16.72K, 234.2K,27.51K,16.63K, 213.5K,26.6K,16.62K, 285.7K,29.88K,17.3K, 280.4K,29.53K,17.36K, 269.1K,29.13K,17.15K, 251.7K,28.31K,17.13K, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0 ) 176,232,1 48,24 [Scenario102,Period_1] 16.1.2006 Jouni Tuomisto This scenario is equal to Scenarios1.0.1 except for that the guaranteed areas are reversed: the guarantee is full with Guar=1 and then it is reduced starting from the most densely populated areas. Thus, Guar=7 has only the least populated areas guaranteed, such as Nuuksio. 18.1.2006 Jouni Tuomisto The run was successful and the data was imported to this node. Table(Output_1,Vehicle_noch,Zone,Length_1,Scenario1_0_1,Period_1)( 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 1090,96,63, 2503,225,128, 5156,433,214, 12.98K,1136,631, 20.91K,1792,963, 23.31K,2018,1124, 25.91K,2282,1230, 28.38K,2489,1463, 33.58K,3025,1569, 39.09K,3433,1903, 46.36K,4141,2282, 52.29K,4553,2498, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 274,31,13, 652,60,25, 1468,116,54, 3495,351,169, 5674,523,289, 6305,575,304, 6945,645,329, 7709,692,378, 9117,769,494, 10.53K,911,478, 12.54K,1123,608, 13.92K,1246,709, 0,0,0, 1,3,1, 11,0,0, 17,0,0, 39,2,3, 57,8,4, 82,9,3, 96,6,3, 107,4,7, 101,12,2, 143,10,6, 163,13,6, 151,19,12, 77,8,2, 146,14,13, 305,27,17, 750,68,32, 1228,95,60, 1330,87,55, 1494,119,75, 1652,132,81, 1928,167,83, 2220,187,128, 2668,252,124, 3040,250,143, 262,21,10, 627,45,17, 1080,77,51, 2776,264,138, 4500,382,206, 5086,453,227, 5673,489,263, 6029,557,319, 7423,670,342, 8458,733,415, 10.35K,911,508, 11.28K,1056,540, 208,16,16, 576,53,27, 1066,113,54, 2842,267,131, 4541,394,216, 5027,446,268, 5588,458,255, 6110,518,294, 7351,640,367, 8429,786,420, 10.19K,856,512, 11.16K,1013,583, 447,41,28, 1131,95,45, 2148,191,111, 5380,439,278, 8497,683,414, 9821,856,466, 10.8K,959,524, 11.96K,1032,569, 13.94K,1245,678, 16.23K,1382,834, 19.28K,1703,962, 21.29K,1919,1068, 610,59,24, 1478,130,69, 2952,242,140, 7131,686,373, 11.43K,1039,561, 12.92K,1120,648, 14.34K,1291,644, 15.99K,1383,755, 18.76K,1598,924, 21.57K,1944,1014, 26.03K,2245,1245, 28.66K,2630,1412, 1421,144,69, 3582,325,187, 7279,639,365, 18.38K,1542,889, 28.96K,2653,1457, 32.47K,2792,1568, 36.19K,3277,1726, 40K,3513,2028, 47.14K,4128,2304, 54.57K,4771,2671, 65.99K,5775,3172, 72.65K,6410,3520, 0,0,0, 80,9,2, 243,18,11, 445,34,20, 1144,94,52, 1754,139,80, 2037,202,94, 2216,200,129, 2448,226,104, 2964,274,139, 3260,303,172, 3952,341,213, 4467,434,220, 434,34,22, 1080,89,43, 2017,187,98, 5152,459,242, 8266,770,416, 9429,802,485, 10.27K,939,509, 11.54K,1047,536, 13.57K,1201,701, 15.73K,1368,753, 18.68K,1648,959, 20.57K,1808,1055, 1130,98,60, 2888,273,146, 5756,518,266, 14.49K,1240,694, 22.91K,2090,1163, 26.12K,2230,1308, 28.53K,2507,1367, 31.87K,2855,1559, 37.79K,3285,1769, 43.16K,3710,2120, 52K,4505,2480, 57.82K,4885,2801, 1407,105,68, 3569,317,178, 7130,618,356, 17.54K,1542,883, 28.01K,2486,1381, 31.66K,2877,1553, 35.13K,3065,1741, 38.71K,3396,1928, 45.74K,4046,2267, 52.43K,4739,2622, 63.45K,5606,3089, 70.77K,6201,3424, 1715,135,88, 4126,336,194, 8449,678,357, 21.19K,1850,1037, 33.88K,2930,1661, 37.36K,3276,1902, 42.29K,3694,2140, 46.13K,4079,2287, 54.53K,4818,2718, 63.62K,5453,3043, 75.85K,6629,3573, 84.14K,7498,4096, 1780,162,93, 4650,403,229, 9439,801,456, 23.18K,2004,1134, 36.63K,3217,1795, 41.33K,3545,2098, 46.03K,3968,2301, 50.67K,4450,2395, 59.52K,5265,2876, 68.91K,6085,3333, 82.71K,7213,3998, 92.37K,8061,4548, 2453,227,130, 6134,485,310, 12.36K,1113,566, 31.3K,2675,1453, 50.01K,4330,2427, 56.06K,4850,2704, 61.97K,5369,3137, 68.26K,6072,3242, 80.45K,6918,4010, 92.76K,8365,4539, 111.5K,9800,5627, 123.9K,10.66K,5983, 0,0,0, 40,4,3, 91,9,3, 190,18,9, 544,35,25, 830,67,47, 868,98,51, 1000,90,56, 1090,75,64, 1292,141,60, 1475,126,76, 1787,197,106, 2021,168,105, 162,12,7, 449,39,16, 847,80,36, 2127,182,113, 3398,295,149, 3827,338,163, 4186,398,210, 4734,397,245, 5546,484,286, 6417,613,296, 7779,739,389, 8474,686,427, 695,71,36, 1780,146,112, 3459,323,161, 8785,779,420, 14.43K,1301,709, 15.56K,1524,749, 17.65K,1537,828, 19.07K,1679,1008, 22.79K,2006,1087, 26.3K,2404,1300, 31.69K,2774,1566, 35.01K,3166,1733, 1724,146,80, 4457,376,202, 8508,758,462, 21.43K,1858,1034, 34.48K,3058,1710, 38.83K,3279,1914, 43.38K,3695,2088, 47.68K,4204,2312, 56.09K,4928,2727, 64.32K,5685,3130, 77.69K,6877,3820, 86.01K,7646,4259, 2052,185,107, 5330,421,224, 10.41K,899,512, 25.94K,2322,1276, 41.67K,3627,2025, 47.08K,4108,2247, 52.08K,4496,2579, 57.47K,5100,2821, 67.99K,6137,3257, 77.89K,6813,3817, 93.49K,8281,4450, 104.1K,9273,4957, 2784,246,138, 7003,625,331, 13.86K,1204,644, 34.1K,3054,1661, 54.79K,4869,2733, 61.98K,5391,2931, 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113.7K,10.11K,5548, 112.7K,9793,5439, 110.8K,9693,5350, 107.9K,9481,5240, 105.6K,9273,5255, 103.3K,8980,5104, 122.7K,10.7K,6094, 121.5K,10.65K,6032, 118.4K,10.3K,5805, 109.5K,9551,5212, 101.1K,8944,4945, 98.13K,8640,4804, 94.77K,8457,4634, 92.43K,8179,4401, 86.97K,7624,4122, 80.58K,7066,3903, 72.25K,6397,3548, 66.2K,5709,3213, 122.4K,10.78K,5980, 120.3K,10.56K,5857, 117.5K,10.36K,5682, 106.5K,9525,5184, 95.26K,8330,4645, 92.4K,8124,4527, 88.96K,7759,4373, 85.32K,7581,4191, 78.44K,6960,3806, 71.3K,6336,3465, 60.84K,5407,2995, 54.05K,4715,2651, 122.7K,10.88K,5990, 119.5K,10.54K,5817, 115.5K,10.21K,5553, 102.6K,8981,4972, 90.37K,8077,4424, 86.46K,7449,4252, 82.48K,7211,4026, 78.02K,6820,3891, 70.07K,6030,3379, 60.9K,5384,2990, 48.48K,4242,2392, 40.12K,3446,2004, 121.9K,10.93K,6100, 119.2K,10.4K,5870, 114.4K,10.14K,5629, 101.4K,9018,4908, 87.84K,7541,4174, 82.49K,7209,4135, 78.02K,6824,3808, 73.5K,6447,3691, 64.07K,5728,3160, 54.89K,4807,2747, 41.72K,3673,2023, 32.49K,2755,1524, 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7656,16,0, 10.85K,72,0, 16.9K,144,0, 21.88K,168,8, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 24,0,0, 24,0,0, 32,0,0, 56,0,0, 104,0,0, 208,8,0, 240,0,0, 0,0,0, 0,0,0, 0,0,0, 16,0,0, 96,0,0, 208,0,0, 240,8,0, 320,0,0, 408,0,0, 752,0,0, 1168,24,0, 1536,0,0, 0,0,0, 0,0,0, 0,0,0, 72,0,0, 704,0,0, 952,0,0, 1424,0,0, 1856,8,0, 2960,16,0, 4256,32,0, 6712,32,0, 8176,88,0, 0,0,0, 8,0,0, 64,0,0, 720,24,0, 2464,32,0, 3072,40,0, 4152,64,8, 5024,72,8, 7344,136,8, 10.29K,120,24, 15.61K,344,24, 19.28K,352,64, 0,0,0, 0,0,0, 72,0,0, 688,8,0, 2384,32,8, 3032,56,8, 4144,56,0, 5264,88,8, 8112,160,0, 10.78K,200,8, 16.34K,288,32, 20.9K,440,48, 0,0,0, 0,0,0, 56,0,0, 760,8,0, 2520,32,0, 3632,32,0, 4688,88,0, 5648,72,8, 8784,104,8, 12.7K,200,24, 19.12K,320,24, 24.54K,408,32, 0,0,0, 8,0,0, 64,0,0, 792,8,0, 2856,24,0, 3688,56,0, 5096,48,0, 6360,72,0, 9712,152,8, 13.93K,200,0, 20.87K,336,24, 27.74K,456,24, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 80,0,0, 424,8,0, 648,8,0, 808,0,0, 1056,8,0, 1632,16,0, 2360,32,0, 3432,32,0, 4784,96,0, 0,0,0, 0,0,0, 0,0,0, 88,0,0, 768,0,0, 1136,0,0, 1456,16,0, 1960,24,0, 2904,32,0, 4976,24,0, 7656,56,16, 9880,136,0, 0,0,0, 0,0,0, 8,0,0, 304,8,0, 1912,8,0, 2928,0,0, 4464,32,0, 5960,8,0, 9744,16,0, 14.17K,48,0, 24.75K,192,0, 31.94K,256,0, 0,0,0, 0,0,0, 16,0,0, 712,0,0, 3840,0,0, 5856,16,0, 7984,40,0, 10.63K,32,0, 17.91K,160,0, 25.68K,248,16, 41.16K,392,0, 53.56K,568,16, 0,0,0, 0,0,0, 16,0,0, 872,0,0, 4656,0,0, 6648,40,0, 9360,24,0, 12.22K,32,0, 20.06K,184,8, 30.15K,232,16, 46.7K,472,16, 60.57K,568,48, 0,0,0, 0,0,0, 16,0,0, 968,0,0, 5272,16,0, 7328,24,0, 10.01K,64,0, 12.99K,48,0, 21.55K,128,0, 32.69K,272,0, 51.81K,472,16, 67.78K,576,32, 0,0,0, 0,0,0, 48,0,0, 1184,8,0, 6240,56,0, 9504,40,0, 13.14K,48,0, 17.71K,136,8, 28.34K,168,0, 41.05K,400,24, 66.61K,672,32, 85.18K,984,32, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 320,0,0, 2296,56,0, 8376,272,24, 27.91K,1624,352, 44.5K,3480,936, 48.82K,3944,1064, 53.37K,4456,1320, 57.42K,5024,1672, 64.52K,6592,2040, 71.03K,7336,2600, 79.49K,9072,3648, 84.26K,9888,4088, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 48,0,0, 832,8,0, 1792,56,0, 2104,80,0, 2536,80,8, 2760,120,0, 3384,144,32, 4016,264,16, 4808,360,40, 5344,288,32, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 16,0,0, 24,0,0, 40,0,0, 40,0,0, 40,0,0, 104,0,0, 144,0,0, 152,8,0, 0,0,0, 0,0,0, 8,0,0, 48,0,0, 64,0,0, 80,0,0, 96,0,0, 184,0,0, 128,8,0, 216,8,0, 216,0,0, 272,16,8, 0,0,0, 120,0,0, 552,16,0, 2128,112,0, 4120,384,48, 3624,320,40, 3816,368,72, 3976,400,72, 4368,504,120, 4704,544,160, 5064,736,176, 5336,864,312, 8,0,0, 104,0,0, 616,16,0, 2064,112,8, 3368,232,16, 3536,312,56, 3664,384,64, 3928,408,96, 4144,504,128, 4552,624,192, 5136,680,248, 5328,808,312, 8,0,0, 304,0,0, 1192,16,0, 4216,240,0, 6848,496,40, 7464,592,88, 8456,768,152, 9000,888,152, 10.02K,1040,176, 11.25K,1152,304, 12.5K,1488,488, 13.18K,1728,584, 24,0,0, 240,0,0, 1216,8,0, 5096,296,0, 8264,648,56, 9240,664,96, 10.43K,776,128, 11K,952,176, 12.68K,1264,224, 14.3K,1432,304, 16.31K,1672,488, 17.18K,2048,576, 40,0,0, 728,0,0, 3080,64,0, 13.27K,632,48, 23.08K,1536,184, 26K,1816,208, 28.93K,2248,336, 31.26K,2544,448, 36.17K,3152,584, 41.14K,3832,936, 46.9K,4776,1264, 50.46K,5384,1544, 0,0,0, 0,0,0, 0,0,0, 24,0,0, 600,0,0, 1576,32,0, 1768,40,0, 2240,32,0, 2432,112,16, 3032,144,8, 3776,176,24, 4504,224,64, 4976,288,24, 16,0,0, 160,0,0, 664,24,0, 2440,176,0, 3960,392,24, 4368,384,48, 4568,456,72, 5072,552,88, 5384,672,120, 6072,824,160, 6408,896,296, 6872,992,408, 0,0,0, 536,8,0, 3056,56,0, 12.44K,600,16, 24.35K,1904,368, 21.07K,1792,248, 22.78K,1976,272, 24.32K,2224,400, 27.16K,2864,576, 29.06K,3368,888, 31.78K,3920,1160, 33.44K,4544,1456, 0,0,0, 616,0,0, 3296,24,0, 13.65K,608,16, 21.98K,1512,176, 25.03K,1976,176, 26.82K,2080,264, 28.99K,2560,360, 32.21K,3184,632, 35.46K,3888,856, 40.02K,4912,1416, 41.98K,5032,1568, 0,0,0, 536,0,0, 3224,32,8, 14.99K,624,16, 24.37K,1624,200, 26.94K,1968,240, 29.5K,2392,360, 31.72K,2712,408, 35.5K,3456,592, 39.18K,4048,952, 44.03K,5112,1336, 45.92K,5736,1784, 16,0,0, 584,0,0, 3944,56,0, 16.01K,744,40, 25.58K,1760,144, 28.68K,2192,296, 31.07K,2448,344, 34.15K,2920,448, 38.03K,3656,696, 42.2K,4352,888, 47.22K,5480,1520, 50.85K,6120,1888, 16,0,0, 832,0,0, 4592,80,0, 17.98K,808,8, 28.72K,2096,280, 31.64K,2440,304, 34.93K,2984,376, 37.88K,3536,528, 42.69K,4192,952, 46.66K,4944,1224, 52.88K,6256,1896, 56K,6784,2000, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 112,0,0, 232,0,0, 264,8,0, 376,8,0, 440,24,0, 448,32,0, 592,24,0, 680,48,0, 728,24,8, 8,0,0, 160,0,0, 1008,8,0, 4864,152,16, 8640,480,24, 9776,616,40, 10.61K,784,80, 11.73K,840,152, 12.98K,1136,224, 14.48K,1480,224, 16.58K,1736,456, 17.89K,1912,568, 48,0,0, 640,0,0, 3464,40,0, 15.09K,688,24, 21.26K,1392,144, 27.22K,2032,216, 30.18K,2376,392, 32.01K,2584,496, 36.27K,3456,736, 39.8K,4104,1064, 44.83K,4976,1528, 48.38K,5688,1856, 56,0,0, 1472,0,0, 8592,152,0, 37K,1560,56, 66.33K,3776,512, 74.42K,4544,672, 82.68K,5536,840, 91.52K,6448,1088, 105.1K,8368,1600, 119.4K,10.28K,2200, 137.5K,12.74K,3496, 148.6K,14.37K,4288, 264,0,0, 2968,64,0, 11.92K,312,8, 48.19K,2296,328, 83.23K,5264,936, 94.19K,6264,1168, 104.5K,7432,1624, 115.3K,8584,1928, 133K,10.67K,2680, 150.8K,12.98K,3720, 173.2K,16.54K,4968, 186.9K,18.72K,5904, 336,8,0, 3400,64,0, 14.14K,376,40, 60.69K,2936,400, 106.7K,6616,1064, 121.3K,7736,1256, 135.5K,9208,1864, 148.2K,10.68K,2224, 172.7K,13.54K,3280, 194.7K,16.57K,4144, 224.3K,20.72K,6416, 242.1K,23.63K,7504, 400,0,0, 4272,56,0, 17.99K,456,24, 74.4K,3392,384, 129.2K,8496,1208, 144.8K,9664,1680, 161K,11.48K,2512, 175.6K,13.31K,2768, 204.4K,16.18K,3768, 231.3K,19.9K,5192, 264.4K,25.22K,7656, 284.9K,27.9K,9096, 0,0,0, 0,0,0, 0,0,0, 136,0,0, 1200,16,0, 2752,80,0, 3584,112,0, 3960,200,8, 4600,224,8, 5856,216,24, 7208,392,32, 8960,552,32, 9696,592,56, 0,0,0, 216,0,0, 1864,24,0, 10.47K,352,8, 20.55K,976,64, 23.1K,1248,104, 26.98K,1640,168, 29.64K,1744,120, 34.69K,2584,216, 39.93K,3112,392, 46.22K,3808,712, 50.62K,4680,912, 0,0,0, 888,0,0, 6184,88,0, 31.37K,1216,32, 50.09K,2904,136, 63.86K,4008,400, 71.46K,4888,400, 77.44K,5504,640, 90.63K,6752,1064, 102.8K,8936,1520, 117.6K,11.11K,2200, 128.5K,12.58K,3152, 56,0,0, 2040,0,0, 12.37K,224,0, 61.77K,2192,120, 116K,6080,624, 133.3K,7448,888, 152K,8896,1200, 169K,10.86K,1688, 198.1K,13.6K,2424, 229K,17.54K,3192, 270.2K,22.32K,4712, 295.5K,25.56K,6184, 256,0,8, 3408,32,0, 16.54K,320,8, 80.26K,3040,328, 148K,8000,1064, 169.9K,9536,1312, 192.2K,11.55K,1888, 212.4K,13.62K,2296, 250.3K,17.78K,3272, 288.9K,22.1K,4512, 339.8K,28.73K,6880, 369.6K,32.99K,8752, 352,8,0, 3896,40,0, 19.37K,408,16, 95.02K,3416,360, 177.7K,9160,1240, 203.2K,11.48K,1792, 228.4K,14.14K,1872, 254.8K,15.67K,2592, 303.2K,21.12K,3944, 347.1K,26.43K,5160, 409.4K,33.51K,8080, 447K,38.58K,9624, 312,0,0, 4808,72,0, 23.92K,520,16, 115K,4792,424, 210.2K,11.46K,1568, 242.2K,14.38K,2024, 274.6K,17.34K,2768, 300.5K,20.11K,3472, 357.4K,26.05K,4872, 409.1K,32.3K,6704, 481.1K,41.31K,10.3K, 524.2K,46.63K,12.6K, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 8,0,0, 48,0,0, 280,4,0, 2076,44,0, 4412,108,16, 5268,168,16, 6164,216,32, 6908,316,60, 8632,420,36, 10.3K,536,80, 12.79K,704,128, 14.34K,900,156, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 4,0,0, 76,0,0, 376,8,0, 852,12,8, 1020,20,4, 1248,40,8, 1344,68,0, 1824,88,16, 2220,92,16, 2816,116,16, 3028,176,20, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 4,0,0, 8,0,0, 12,0,0, 24,0,0, 24,0,0, 48,0,0, 52,0,0, 60,0,0, 0,0,0, 4,0,0, 24,0,0, 160,0,4, 424,20,0, 420,16,4, 496,8,4, 468,28,8, 616,36,4, 696,60,20, 704,52,12, 896,64,24, 0,0,0, 8,0,0, 68,0,0, 572,24,0, 1364,32,0, 1580,80,4, 1836,80,12, 2040,84,4, 2580,152,4, 2808,172,16, 3152,216,32, 3556,260,48, 0,0,0, 4,0,0, 56,0,0, 612,16,0, 1296,36,0, 1576,64,0, 1816,52,8, 2028,88,0, 2468,152,8, 2768,188,24, 3096,184,36, 3472,224,36, 0,0,0, 4,0,0, 108,0,0, 848,4,0, 1788,40,4, 2428,72,4, 2668,96,8, 3216,84,12, 4064,164,16, 4496,228,28, 5704,340,44, 6320,360,52, 0,0,0, 4,0,0, 84,0,0, 940,12,0, 2404,68,8, 2832,72,4, 3476,128,4, 4112,136,20, 4988,140,20, 5832,248,28, 7472,320,40, 8536,464,60, 0,0,0, 4,0,0, 140,0,0, 2032,8,0, 4720,80,4, 6012,96,8, 7276,188,8, 9056,212,24, 11.48K,368,28, 14.06K,412,56, 18.2K,712,44, 20.31K,780,120, 0,0,0, 0,0,0, 0,0,0, 16,0,0, 272,0,0, 368,12,8, 592,28,0, 656,16,4, 656,28,0, 884,44,0, 1004,48,8, 1208,56,16, 1316,88,8, 0,0,0, 12,0,0, 92,4,0, 964,8,0, 2096,60,4, 2592,80,4, 2920,132,8, 3376,128,12, 4236,144,24, 5008,228,24, 5940,348,48, 6404,392,52, 0,0,0, 12,0,0, 268,0,0, 2636,24,0, 5868,188,8, 7228,184,20, 8172,252,20, 9352,384,36, 11.37K,476,64, 13.39K,604,88, 15.42K,908,156, 16.8K,1064,188, 0,0,0, 20,0,0, 264,4,0, 2760,28,0, 6128,196,4, 7480,196,12, 8716,256,32, 9636,344,56, 12.2K,508,48, 14.3K,692,84, 16.96K,972,140, 18.97K,1180,192, 0,0,0, 20,0,0, 288,0,0, 2848,20,4, 6996,144,12, 7828,196,4, 9380,284,32, 10.65K,364,48, 13.92K,556,44, 16.38K,728,72, 19.94K,996,124, 21.62K,1252,188, 0,0,0, 12,0,0, 264,4,0, 2888,40,4, 6768,112,16, 8320,256,16, 9964,268,24, 11.32K,280,28, 14.38K,576,32, 16.7K,728,96, 20.8K,932,164, 22.99K,1208,224, 4,0,0, 16,0,0, 272,4,0, 3352,44,0, 8324,180,0, 10.2K,236,16, 11.83K,296,20, 13.87K,424,12, 17.57K,588,72, 21.06K,880,80, 26.88K,1220,192, 30.14K,1440,244, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 76,0,0, 164,4,0, 160,4,0, 184,0,0, 252,4,0, 312,8,0, 396,12,4, 500,28,4, 604,20,4, 0,0,0, 4,0,0, 24,0,0, 180,4,0, 632,20,0, 744,32,0, 928,40,8, 1088,16,0, 1572,52,8, 1832,64,8, 2308,116,4, 2424,104,16, 0,0,0, 24,0,0, 128,0,0, 1292,40,0, 3388,52,0, 3692,128,4, 4428,140,20, 5096,156,28, 6576,204,12, 7964,328,24, 9788,476,40, 10.76K,592,84, 20,0,0, 64,0,0, 360,0,4, 2748,44,12, 6324,176,28, 7900,188,44, 9488,248,44, 10.97K,412,52, 13.81K,496,88, 16.29K,696,100, 20.66K,932,196, 23.32K,1152,252, 4,0,0, 60,0,0, 356,12,4, 2880,60,4, 7056,176,20, 9204,264,40, 10.4K,272,44, 12.15K,400,56, 15.58K,632,88, 19.01K,756,140, 24K,1076,152, 27.44K,1368,244, 16,0,0, 68,0,0, 440,8,0, 3364,52,12, 8828,216,28, 10.94K,304,32, 12.98K,316,52, 15.3K,440,64, 20.09K,624,124, 24.36K,924,132, 31.75K,1260,196, 36.67K,1376,252, 0,0,0, 104,0,0, 408,4,0, 3800,52,8, 9812,244,36, 12.25K,292,36, 14.66K,512,44, 17.36K,424,108, 22.24K,752,124, 27.42K,1060,144, 35.17K,1508,188, 39.78K,1768,256, 0,0,0, 0,0,0, 4,0,0, 68,0,0, 728,32,0, 1984,32,12, 2284,48,4, 2876,72,4, 3368,100,0, 4732,144,12, 5520,176,8, 7560,280,48, 8672,356,32, 0,0,0, 4,0,0, 128,4,0, 1680,16,0, 4420,124,0, 5632,116,0, 6884,136,4, 8272,168,24, 11.27K,252,20, 13.93K,468,60, 18.18K,504,76, 20.84K,824,108, 0,0,0, 36,0,0, 404,0,0, 4604,36,0, 13.87K,224,16, 17.26K,336,16, 20.59K,424,40, 24.89K,520,32, 32.72K,892,88, 40.25K,1112,104, 52.42K,1700,240, 58.64K,2232,244, 4,0,0, 96,0,0, 744,4,4, 7824,148,4, 21.9K,516,32, 27.32K,608,44, 33.26K,748,56, 38.83K,1004,80, 51.57K,1332,164, 64.83K,1900,200, 83.65K,3096,476, 97.85K,3712,524, 4,0,0, 116,0,0, 872,4,0, 9172,156,12, 25.05K,528,36, 31.63K,624,52, 38.51K,824,92, 45.58K,1080,136, 60.74K,1700,176, 75.24K,2296,284, 98.35K,3344,396, 113.8K,4060,632, 4,0,0, 108,0,0, 932,0,0, 10.4K,152,4, 28.23K,504,44, 35.65K,668,44, 43.51K,944,108, 51.85K,1252,100, 69.33K,1876,220, 87.51K,2620,304, 115.9K,3940,580, 133.2K,4760,600, 8,0,0, 140,0,0, 1096,28,0, 13K,208,8, 35.47K,616,76, 44.13K,972,84, 54.2K,1260,108, 64.53K,1568,200, 84.51K,2320,244, 106K,3076,324, 139.3K,4628,636, 159.2K,6132,820, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 844,44,4, 2676,200,24, 4972,452,112, 8116,1180,556, 9576,1672,924, 9892,1744,1192, 10.34K,1900,1172, 10.48K,1952,1368, 10.68K,2096,1648, 10.75K,2404,1892, 11.44K,2444,2148, 11.79K,2492,2416, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 92,0,0, 352,4,0, 980,72,12, 1488,112,36, 1524,156,36, 1608,184,52, 1692,180,52, 1892,220,92, 2108,228,104, 2360,272,168, 2536,340,188, 0,0,0, 0,0,0, 0,0,0, 4,0,0, 44,0,0, 68,0,0, 104,0,0, 136,12,0, 124,0,0, 124,4,0, 152,8,0, 120,16,0, 140,20,4, 0,0,0, 12,0,0, 16,0,0, 168,8,4, 300,24,0, 292,16,0, 332,20,8, 368,16,8, 424,36,4, 356,48,16, 476,52,12, 448,48,24, 88,0,0, 300,12,0, 460,40,4, 844,128,52, 992,152,112, 1048,180,152, 1140,192,116, 1256,200,132, 1404,204,188, 1452,200,196, 1560,236,248, 1676,260,224, 40,0,0, 292,12,0, 428,64,4, 720,108,64, 996,160,108, 1000,176,124, 1244,148,140, 1324,180,148, 1356,216,180, 1464,200,168, 1468,252,216, 1604,220,208, 116,0,0, 516,12,4, 820,68,8, 1500,236,120, 2104,296,192, 2232,276,216, 2344,328,224, 2424,364,252, 2620,376,332, 2996,416,380, 3104,496,376, 3208,516,352, 132,4,0, 616,28,0, 1228,96,4, 2280,244,80, 2996,404,220, 3220,492,256, 3348,508,284, 3716,516,264, 3920,520,400, 4184,644,440, 4368,744,484, 4508,688,504, 268,16,0, 1360,84,8, 3128,240,32, 6112,680,200, 7800,1152,536, 8020,1228,584, 8564,1340,732, 8796,1348,784, 9592,1492,988, 10.03K,1744,1128, 10.89K,1908,1264, 10.91K,1956,1392, 0,0,0, 8,0,0, 44,0,0, 152,8,0, 1052,56,8, 1380,136,16, 1508,116,32, 1584,168,20, 1780,196,24, 1940,212,60, 2056,248,60, 2204,348,128, 2300,408,152, 76,0,0, 332,12,0, 588,64,0, 1032,172,52, 1160,176,148, 1316,232,156, 1408,252,152, 1424,220,172, 1464,256,252, 1536,236,252, 1684,296,308, 1648,300,272, 300,0,0, 1716,52,8, 3164,240,24, 4428,708,232, 6484,1096,736, 4984,1068,772, 5116,1148,772, 5396,1220,832, 5500,1240,1028, 5624,1316,1256, 5608,1360,1480, 5784,1256,1508, 332,4,0, 1792,68,0, 3704,276,32, 5428,852,268, 6116,1204,656, 6076,1224,776, 6320,1444,884, 6352,1472,1044, 6776,1396,1204, 6784,1460,1444, 6664,1452,1540, 7020,1668,1848, 372,0,0, 1784,76,4, 4064,268,16, 5944,904,208, 6224,1304,676, 6548,1336,824, 6656,1392,916, 6760,1448,1028, 7112,1516,1292, 7464,1636,1444, 6980,1692,1616, 7700,1752,1816, 300,0,0, 2120,92,4, 3912,320,28, 5780,912,336, 6584,1336,716, 6560,1268,876, 6864,1484,904, 6896,1548,1128, 6952,1632,1260, 7136,1724,1484, 7108,1744,1708, 7256,1724,1808, 424,4,0, 2112,100,0, 3928,372,40, 6020,1032,348, 6916,1312,780, 7344,1424,900, 7520,1392,1052, 7636,1560,1180, 7792,1556,1412, 8452,1792,1548, 8480,1748,1812, 8852,1800,1980, 0,0,0, 0,0,0, 20,0,0, 48,4,0, 304,16,0, 536,36,4, 520,40,16, 628,48,16, 656,56,8, 888,72,12, 892,52,12, 972,116,20, 1160,128,44, 84,4,0, 676,28,0, 1400,100,16, 2376,288,104, 2712,436,220, 2720,472,196, 2792,512,316, 2980,556,296, 3204,608,480, 3512,648,492, 3540,644,632, 3660,628,620, 372,4,4, 1800,44,12, 3492,276,28, 5956,848,232, 7068,1108,520, 7308,1336,744, 7644,1272,824, 7728,1452,928, 8148,1600,1072, 8452,1684,1352, 8656,1800,1612, 9044,1804,1744, 832,16,0, 4400,200,8, 9064,572,92, 16.9K,2052,576, 19.94K,3172,1464, 21.09K,3544,1776, 21.74K,3772,2184, 22.11K,3884,2304, 23.52K,4328,2904, 23.94K,4680,3408, 25.61K,5008,3944, 26.04K,5324,4464, 1112,60,0, 4976,228,36, 10.22K,716,156, 20.04K,2472,956, 25.13K,3616,1876, 26.64K,3960,2092, 27.59K,4188,2364, 28.68K,4492,2592, 30.22K,5264,3400, 31.47K,5476,3956, 33.28K,6132,4568, 34.15K,6252,4952, 1424,28,0, 6192,296,20, 13.78K,900,132, 26.82K,3036,984, 34.26K,4840,2192, 35.94K,5356,2704, 37.06K,5724,3004, 39.1K,6168,3328, 41.44K,6804,4128, 42.66K,7632,5040, 45.21K,8404,5620, 45.86K,8852,6472, 1768,56,0, 7708,324,32, 15.87K,1156,136, 28.49K,3680,1272, 34.42K,5476,2800, 36.11K,6068,3208, 36.97K,6296,3656, 38.7K,6404,4304, 40.45K,7604,5324, 41.95K,7912,5792, 43.69K,8820,6896, 44.59K,8928,7848, 0,0,0, 16,0,0, 272,4,0, 1012,16,0, 3224,160,0, 5484,352,40, 6148,376,48, 6952,596,92, 7264,660,116, 8328,756,124, 9444,924,196, 10.28K,1128,312, 11.11K,1244,372, 216,4,0, 1348,16,0, 3576,180,8, 8640,736,116, 12.42K,1412,400, 13.16K,1600,544, 13.76K,1764,652, 14.91K,1912,724, 16.28K,2152,1148, 17.56K,2476,1248, 18.7K,2900,1700, 19.61K,2952,1768, 504,8,0, 3816,100,12, 9468,484,48, 20.13K,2108,520, 25.98K,3136,1140, 28.34K,3804,1584, 30.28K,4192,1820, 31.33K,4436,2336, 33.29K,5040,2724, 35.59K,5668,3336, 38.46K,6408,4196, 40.46K,6716,4684, 1216,12,0, 7188,232,16, 17.93K,984,108, 40.12K,3932,1032, 53.03K,6528,2508, 56.18K,7688,3028, 58.84K,8164,3328, 61.18K,8780,4264, 65.74K,10.03K,5100, 69.66K,11.24K,6108, 72.96K,12.74K,7712, 75.91K,13.26K,8700, 1604,20,0, 8620,300,28, 21.11K,1108,164, 46.48K,4860,1272, 61.28K,7776,3168, 64.42K,8860,3320, 66.94K,9828,4440, 69.8K,10.28K,4656, 73.78K,11.65K,6328, 76.8K,12.96K,7636, 80.72K,14.62K,9044, 83.64K,15.36K,10.14K, 1812,36,4, 9872,284,60, 25.07K,1424,204, 55.23K,5500,1428, 70.24K,9488,3440, 74.57K,10.33K,4484, 77.54K,10.99K,4900, 80.52K,12.02K,5676, 83.76K,13.68K,6868, 87.41K,15.24K,8604, 91.25K,17.21K,10.6K, 94.38K,18.21K,11.79K, 2408,64,4, 12.09K,496,32, 29.84K,1816,240, 62.69K,6616,1876, 79.69K,10.75K,4200, 83.51K,11.88K,5212, 86.71K,12.84K,5940, 89.94K,13.99K,7100, 95.45K,15.76K,8752, 99.9K,17.18K,10.25K, 104.4K,19.15K,12.45K, 107.3K,20.17K,14.04K, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 3097,387,215, 5154,723,459, 7035,1140,787, 8799,1806,1601, 9731,2087,2052, 10.03K,2295,2294, 9920,2379,2347, 10.2K,2400,2487, 10.56K,2373,2609, 10.96K,2428,2762, 11.11K,2563,2967, 11.25K,2692,3109, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 260,27,15, 612,71,27, 1030,133,47, 1772,258,184, 2116,356,270, 2200,359,247, 2248,396,293, 2339,414,356, 2432,442,384, 2604,498,421, 2755,497,444, 2870,530,502, 0,0,0, 5,3,1, 25,1,2, 57,2,0, 96,12,9, 107,17,10, 104,18,9, 81,13,16, 102,20,15, 116,28,11, 102,29,12, 120,26,18, 112,25,25, 99,10,2, 194,25,15, 356,39,22, 585,79,37, 636,91,79, 636,86,68, 636,138,89, 670,147,101, 659,141,105, 672,141,129, 693,163,145, 692,160,150, 374,48,20, 782,94,33, 1104,134,91, 1767,293,218, 1964,326,300, 2049,348,275, 2089,384,345, 2073,404,366, 2252,441,377, 2185,468,439, 2251,487,482, 2292,534,488, 362,34,21, 736,93,50, 1039,150,102, 1758,286,216, 2042,359,287, 2108,374,316, 2036,389,307, 2094,400,388, 2158,422,405, 2179,496,427, 2250,487,468, 2183,561,519, 721,78,43, 1279,163,88, 1889,287,163, 2967,466,375, 3510,619,499, 3576,681,537, 3581,712,600, 3714,705,651, 3746,800,741, 3763,789,741, 3722,847,822, 3894,863,903, 869,99,42, 1790,215,128, 2663,347,228, 3939,686,528, 4733,809,649, 4781,826,774, 4759,938,778, 4914,998,836, 5101,1029,941, 5125,1135,1002, 5143,1157,1114, 5305,1320,1231, 2124,262,107, 4059,500,291, 5911,881,510, 8682,1545,1155, 10.11K,1947,1696, 10.47K,1985,1733, 10.37K,2113,1845, 10.7K,2135,2055, 11.01K,2282,2176, 11.09K,2432,2353, 11.52K,2643,2706, 11.44K,2703,2694, 0,0,0, 120,19,6, 325,32,15, 517,52,32, 1609,264,158, 1924,344,235, 2030,343,303, 2003,407,311, 2081,343,323, 2079,438,395, 2149,449,423, 2273,497,453, 2292,495,520, 385,47,25, 687,97,42, 920,154,98, 1212,218,206, 1401,289,275, 1359,315,311, 1375,296,292, 1421,325,343, 1525,336,329, 1568,341,377, 1543,363,411, 1533,374,418, 1781,185,98, 2928,440,251, 3729,669,458, 4450,995,951, 7047,1532,1510, 4615,1103,1282, 4698,1109,1325, 4607,1157,1442, 4745,1223,1459, 4863,1214,1535, 4823,1270,1591, 4746,1229,1669, 1995,202,109, 3548,470,283, 4381,779,544, 5273,1171,1128, 5694,1344,1461, 5748,1380,1547, 5697,1408,1532, 5699,1391,1651, 5895,1538,1765, 5896,1504,1814, 6001,1534,1865, 5872,1599,1940, 2163,205,116, 3662,493,285, 4709,781,550, 5618,1250,1212, 5895,1374,1556, 5827,1457,1621, 6005,1516,1719, 5987,1500,1746, 6005,1527,1890, 6059,1546,1933, 6035,1646,2110, 6199,1611,2070, 2146,253,125, 3729,546,312, 4735,840,564, 5496,1319,1195, 5998,1474,1623, 5876,1460,1719, 5859,1464,1708, 6052,1489,1675, 5987,1540,1873, 6051,1554,1943, 6074,1576,1996, 5982,1620,2125, 2383,245,162, 3962,531,359, 5207,912,645, 6679,1368,1237, 7220,1629,1657, 7291,1647,1804, 7415,1666,1893, 7484,1699,1900, 7573,1799,2051, 7694,1834,2103, 7966,1871,2245, 8004,1958,2310, 0,0,0, 87,5,6, 204,21,8, 428,46,23, 808,117,64, 1056,158,90, 1109,182,93, 1175,185,133, 1158,163,160, 1190,218,169, 1328,251,179, 1360,268,223, 1353,273,220, 806,93,40, 1587,166,108, 2151,368,199, 2916,562,470, 3369,668,589, 3552,686,707, 3682,682,688, 3637,698,757, 3774,744,829, 3783,799,865, 3951,880,936, 4133,893,953, 2221,248,122, 4210,546,329, 5530,909,557, 7643,1452,1220, 8891,1862,1655, 8983,1865,1775, 8997,1860,1857, 9304,1979,2039, 9472,2027,2154, 9844,2211,2251, 9972,2290,2474, 10.2K,2352,2546, 5766,597,326, 10.91K,1373,791, 15K,2341,1547, 20.21K,3808,3199, 22.73K,4721,4463, 23.19K,4833,4800, 23.67K,5020,4828, 24.08K,5319,5363, 24.89K,5492,5720, 25.48K,5681,5957, 25.86K,6084,6523, 26.17K,6247,6730, 6875,726,418, 13.02K,1634,921, 18.49K,2807,1825, 25.68K,4677,3825, 28.94K,5742,5124, 29.51K,5973,5546, 30.24K,6236,5866, 30.56K,6477,6105, 31.57K,6860,6631, 32.11K,7078,7202, 32.97K,7538,7868, 32.95K,7805,8334, 9037,974,502, 17.31K,2105,1241, 24.79K,3678,2339, 34.27K,6309,4975, 38.13K,7783,7015, 38.96K,8235,7306, 39.73K,8345,7682, 40.65K,8619,8359, 41.23K,9272,9192, 42.46K,9680,9612, 42.86K,9987,10.66K, 43.37K,10.42K,11.31K, 9991,1161,569, 18.1K,2381,1375, 24.78K,3811,2631, 33.11K,6469,5456, 36.7K,7707,7220, 37.31K,8205,7839, 37.95K,8289,8363, 38.49K,8737,8615, 39.48K,9142,9298, 39.98K,9398,10.13K, 40.59K,9698,10.78K, 41.15K,10.07K,11.16K, 0,0,0, 1275,123,48, 2964,259,124, 4820,527,317, 7583,1041,621, 9477,1485,966, 9792,1590,1134, 10.25K,1687,1156, 10.76K,1727,1217, 11.09K,1986,1462, 11.5K,2112,1580, 12.11K,2447,1955, 12.32K,2455,2082, 2799,270,138, 5706,654,347, 8888,1172,654, 13.84K,2261,1610, 16.49K,2896,2192, 17.06K,3070,2500, 17.62K,3274,2758, 17.73K,3323,2863, 18.63K,3568,3225, 19.16K,3921,3456, 19.8K,4279,3884, 20.49K,4345,4204, 6581,633,337, 12.84K,1644,853, 18.99K,2674,1564, 28.54K,4908,3578, 32.84K,6179,4956, 34.05K,6502,5695, 34.69K,6815,5979, 35.49K,7228,6302, 36.9K,7718,7021, 37.63K,8007,7661, 38.41K,8560,8282, 39.34K,8843,8811, 12.39K,1218,659, 24.65K,2915,1525, 37.32K,5356,3180, 53.31K,9360,6789, 60.78K,11.75K,9848, 63.1K,12.58K,10.79K, 64.12K,13.08K,11.44K, 65.33K,13.49K,12.14K, 67.5K,14.51K,13.37K, 69.14K,15.21K,14.41K, 70.96K,15.76K,16.01K, 71.87K,16.36K,16.79K, 14.27K,1453,758, 28.14K,3478,1995, 41.78K,6073,3670, 58.91K,10.67K,8073, 66.24K,13.32K,11.36K, 67.88K,14.07K,12.19K, 69.52K,14.51K,13.21K, 70.42K,15.02K,13.94K, 72.25K,15.91K,15.23K, 73.9K,16.82K,16.47K, 75.29K,17.65K,17.92K, 76.57K,17.99K,18.95K, 16.54K,1739,913, 32.8K,3971,2212, 48.33K,6756,4303, 66.89K,12.25K,9207, 75.39K,15.56K,13.09K, 76.89K,16.02K,14.07K, 78.43K,16.48K,15.22K, 80.08K,17.31K,15.91K, 81.75K,18.12K,17.84K, 83.28K,19.11K,19.1K, 85.38K,19.88K,20.97K, 86.46K,20.62K,21.81K, 19.32K,2038,976, 37.52K,4607,2500, 54.45K,7867,4945, 76.47K,13.94K,10.54K, 85.83K,17.29K,15.35K, 87.85K,18.18K,16.33K, 89.19K,18.62K,17.17K, 90.92K,19.64K,18.34K, 93.14K,20.4K,20.11K, 94.9K,21.46K,21.66K, 97.27K,22.58K,23.9K, 99.07K,22.95K,24.54K, 52.13K,4594,2495, 52.02K,4467,2529, 51.51K,4545,2578, 51.55K,4482,2542, 51.55K,4543,2609, 51.75K,4584,2559, 51.95K,4516,2518, 51.85K,4526,2519, 51.69K,4489,2536, 51.25K,4586,2521, 51.88K,4506,2450, 51.4K,4643,2537, 51.6K,4453,2531, 51.72K,4475,2482, 51.67K,4543,2524, 51.76K,4551,2510, 51.78K,4533,2590, 51.83K,4516,2531, 51.62K,4531,2516, 51.4K,4656,2483, 51.71K,4548,2554, 51.92K,4498,2498, 51.42K,4585,2409, 51.71K,4442,2464, 51.81K,4505,2551, 51.74K,4570,2639, 52.03K,4575,2473, 51.81K,4461,2540, 51.77K,4524,2588, 51.52K,4590,2426, 51.43K,4649,2524, 51.59K,4588,2503, 51.46K,4587,2538, 51.97K,4558,2497, 52.06K,4483,2610, 51.46K,4419,2567, 51.83K,4378,2524, 51.46K,4542,2465, 51.86K,4444,2535, 51.85K,4512,2521, 52.08K,4531,2611, 51.63K,4632,2548, 51.69K,4559,2502, 51.81K,4576,2461, 52.02K,4572,2560, 51.79K,4542,2548, 51.71K,4463,2570, 51.48K,4574,2575, 51.34K,4469,2556, 51.52K,4475,2533, 51.5K,4497,2560, 51.58K,4527,2501, 51.79K,4566,2586, 51.82K,4578,2620, 51.85K,4599,2535, 51.45K,4656,2532, 52.01K,4555,2511, 51.48K,4560,2509, 51.56K,4525,2481, 51.52K,4435,2445, 51.58K,4570,2474, 51.78K,4533,2558, 51.91K,4499,2452, 51.59K,4602,2547, 52.1K,4644,2515, 51.66K,4435,2517, 51.95K,4558,2490, 51.75K,4525,2543, 51.57K,4542,2522, 52.15K,4524,2465, 51.99K,4563,2588, 51.55K,4584,2569, 51.51K,4609,2499, 50.51K,4334,2523, 48.96K,4200,2303, 46.53K,4098,2265, 39.04K,3371,1855, 31.3K,2687,1536, 28.59K,2658,1416, 25.92K,2262,1336, 23.3K,2052,1135, 17.96K,1561,907, 13.14K,1113,628, 5096,446,244, 0,0,0, 13.95K,1247,703, 13.98K,1212,670, 14.03K,1245,721, 14.15K,1210,703, 13.91K,1320,675, 14.06K,1227,689, 14.17K,1194,708, 14.08K,1183,658, 14.12K,1220,686, 13.86K,1237,662, 14.13K,1280,697, 13.91K,1265,698, 14.12K,1213,682, 14.15K,1213,694, 13.95K,1263,671, 13.88K,1224,713, 13.91K,1235,672, 13.87K,1235,685, 13.9K,1156,685, 13.99K,1233,672, 13.86K,1148,719, 13.85K,1227,667, 14.36K,1203,734, 13.96K,1198,751, 13.81K,1251,682, 14K,1244,674, 13.97K,1171,691, 13.91K,1212,696, 13.99K,1218,671, 13.72K,1224,693, 14.05K,1196,666, 14.04K,1285,703, 14.12K,1149,713, 13.93K,1236,688, 14.04K,1198,692, 14.07K,1236,699, 13.96K,1201,692, 14.07K,1210,692, 14.1K,1197,732, 13.98K,1140,674, 14.14K,1206,676, 13.95K,1256,681, 14K,1264,598, 14.12K,1235,677, 13.96K,1178,651, 13.87K,1222,710, 13.96K,1231,637, 14.31K,1177,677, 14.03K,1205,661, 14.08K,1207,722, 13.96K,1217,679, 13.85K,1224,707, 13.96K,1144,669, 13.85K,1233,664, 13.97K,1247,683, 14.19K,1236,726, 14.03K,1224,666, 13.82K,1278,666, 13.76K,1241,643, 13.75K,1259,660, 13.72K,1169,715, 13.99K,1263,701, 14.02K,1192,705, 13.93K,1214,738, 14.09K,1173,682, 14.07K,1255,700, 13.97K,1280,680, 13.93K,1236,670, 13.78K,1218,676, 13.89K,1211,697, 14K,1165,691, 13.78K,1250,662, 14.17K,1261,707, 13.87K,1179,636, 13.27K,1209,697, 12.39K,1111,617, 10.59K,916,550, 8321,776,428, 7709,657,385, 6986,599,332, 6303,559,287, 4728,427,221, 3419,303,171, 1366,144,58, 0,0,0, 72.53K,6427,3587, 72.97K,6298,3670, 72.62K,6425,3591, 73.68K,6332,3527, 72.57K,6331,3520, 72.92K,6395,3592, 72.68K,6360,3528, 73.08K,6373,3578, 72.69K,6357,3581, 72.46K,6596,3524, 72.55K,6333,3637, 72.55K,6375,3446, 72.58K,6299,3581, 72.98K,6503,3654, 72.81K,6263,3660, 72.1K,6335,3536, 72.09K,6335,3442, 71.28K,6267,3433, 71.56K,6363,3525, 71.46K,6281,3480, 71.76K,6257,3494, 70.74K,6257,3581, 70.52K,6316,3453, 69.73K,6179,3397, 70.09K,6113,3353, 73.05K,6333,3490, 72.49K,6412,3508, 71.7K,6388,3511, 70.16K,6088,3369, 68.36K,6164,3342, 67.99K,5813,3431, 67.09K,6026,3256, 66.58K,6034,3245, 65.65K,5760,3230, 64.24K,5545,3152, 62.87K,5607,3082, 61.58K,5570,3014, 73.03K,6558,3526, 72.53K,6403,3480, 71.17K,6231,3540, 70.14K,6125,3541, 68.47K,6013,3336, 67.33K,6016,3326, 67.38K,5933,3282, 66.93K,5809,3267, 65.57K,5800,3211, 64.67K,5707,3167, 62.72K,5544,2941, 61.77K,5439,3116, 72.35K,6531,3516, 72.12K,6252,3557, 70.62K,6068,3521, 67.92K,5990,3276, 64.42K,5716,3199, 63.3K,5571,3077, 62.26K,5671,3015, 60.58K,5322,3024, 59.16K,5004,2882, 57.1K,5118,2769, 53.55K,4588,2581, 51.39K,4421,2524, 72.71K,6389,3573, 71.26K,6212,3566, 69.78K,6214,3387, 65.61K,5803,3214, 60.94K,5350,2997, 60.23K,5283,2942, 58.41K,5110,2741, 57.41K,5035,2696, 53.82K,4758,2630, 51.14K,4568,2420, 47K,4118,2257, 43.84K,3877,2216, 71.13K,6084,3579, 69.29K,5964,3404, 65.38K,5825,3146, 54.54K,4866,2633, 43.69K,3836,2156, 40.12K,3516,1983, 36.52K,3164,1714, 32.7K,2931,1607, 25.73K,2281,1202, 18.25K,1545,882, 7242,612,370, 0,0,0, 124.2K,10.92K,6033, 124K,10.83K,6089, 123.9K,10.99K,5903, 123.4K,10.85K,5952, 122.5K,10.72K,6071, 122.3K,10.72K,5926, 122K,10.76K,5996, 122.1K,10.74K,5921, 121.6K,10.78K,5943, 121.6K,10.61K,6025, 121.2K,10.77K,5860, 120.1K,10.5K,5957, 119.8K,10.46K,5845, 124.4K,10.97K,6203, 123.2K,10.79K,5958, 122.4K,10.82K,6012, 119.2K,10.55K,5896, 116.5K,10.3K,5607, 114.8K,10.04K,5679, 113.7K,10.11K,5548, 112.7K,9793,5439, 110.8K,9693,5350, 107.9K,9481,5240, 105.6K,9273,5255, 103.3K,8980,5104, 122.7K,10.7K,6094, 121.5K,10.65K,6032, 118.4K,10.3K,5805, 109.5K,9551,5212, 101.1K,8944,4945, 98.13K,8640,4804, 94.77K,8457,4634, 92.43K,8179,4401, 86.97K,7624,4122, 80.58K,7066,3903, 72.25K,6397,3548, 66.2K,5709,3213, 122.4K,10.78K,5980, 120.3K,10.56K,5857, 117.5K,10.36K,5682, 106.5K,9525,5184, 95.26K,8330,4645, 92.4K,8124,4527, 88.96K,7759,4373, 85.32K,7581,4191, 78.44K,6960,3806, 71.3K,6336,3465, 60.84K,5407,2995, 54.05K,4715,2651, 122.7K,10.88K,5990, 119.5K,10.54K,5817, 115.5K,10.21K,5553, 102.6K,8981,4972, 90.37K,8077,4424, 86.46K,7449,4252, 82.48K,7211,4026, 78.02K,6820,3891, 70.07K,6030,3379, 60.9K,5384,2990, 48.48K,4242,2392, 40.12K,3446,2004, 121.9K,10.93K,6100, 119.2K,10.4K,5870, 114.4K,10.14K,5629, 101.4K,9018,4908, 87.84K,7541,4174, 82.49K,7209,4135, 78.02K,6824,3808, 73.5K,6447,3691, 64.07K,5728,3160, 54.89K,4807,2747, 41.72K,3673,2023, 32.49K,2755,1524, 121.3K,10.73K,6078, 118.6K,10.34K,5791, 111.8K,9853,5459, 92.93K,8078,4528, 74.52K,6515,3643, 68.29K,5955,3288, 62.09K,5469,3100, 56.2K,4982,2656, 43.34K,3782,2015, 31.15K,2739,1560, 12.34K,1056,611, 0,0,0, 149.5K,13.08K,7274, 148.9K,13.15K,7360, 149.4K,13.14K,7274, 149K,12.82K,7402, 148.4K,13.12K,7180, 148.3K,13.06K,7160, 147.7K,13.01K,7176, 147.8K,12.94K,7289, 147.6K,13.16K,7342, 147.8K,13.11K,7119, 147.3K,13.07K,6995, 147.3K,12.73K,7238, 147.2K,13.16K,7220, 149K,13.15K,7429, 148.5K,13.14K,7279, 148.1K,12.88K,7113, 146.7K,12.94K,7229, 145.4K,12.71K,7008, 145K,12.64K,7133, 144.1K,12.56K,7025, 144K,12.66K,7006, 143.4K,12.31K,6940, 142.4K,12.4K,7151, 141.9K,12.16K,6825, 140.3K,12.18K,6968, 147.4K,13.04K,7230, 147.1K,12.75K,7298, 145.5K,12.69K,7173, 139.9K,12.23K,6923, 135.4K,11.8K,6530, 133.2K,11.55K,6606, 131.3K,11.61K,6358, 130.4K,11.36K,6388, 126.5K,11K,6262, 122.2K,10.79K,6032, 117.1K,10.29K,5720, 113.8K,9936,5670, 147.3K,13.05K,7204, 144.3K,12.53K,7094, 140K,12.27K,7027, 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0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0 ) 296,312,1 48,24 2,102,90,476,364 2,248,258,664,303,0,MIDM 2,104,114,867,351,0,MIDM [Scenario1_0_1,Period_1] [Scenario1_0_1,Output_1] concat(Scen1_0_1,Scen102,Scenario1_0_1,Scenario102,Scenario_1) 296,232,1 48,24 2,248,258,664,303,0,MIDM [Scenario1_0_1,Period_1] [Scenario_1,Output_1] 1..size(Scenario1_0_1)+size(Scenario102) 296,264,1 48,12 Choice(Self,1,False) 448,232,1 48,24 2,11,508,476,224 [Formnode Choose_scen1] 52425,39321,65535 ['Article + sensitivity','Inverse guarantee','Only 9 seat vehicles'] A table of different scenarios to be studied. Each row contains the values for the input variables used for the scenario. Table(Input_var,Scenario5)( 0,0.1,0.25,0.5,0.75, 7,7,7,7,7, 0,0,0,0,0, 0,0,0,0,0, 0,0,0,0,0 ) ['Composite fraction','Guarantee level','Lim'] 568,152,1 48,24 2,171,44,698,560,0,MIDM 2,12,40,610,489,0,MIDM 52425,39321,65535 [Input_var,Scenario] [Input_var,Scenario5] Index for a list of scenarios to be modelled. [1,2,3,4,5] 568,184,1 48,12 [1,2,3,4,5] 29.8.2005 Jouni Tuomisto Otherwise similar than previous runs, but it was assumed that all vehicles have 9 seats. This is a sensitivity analysis to see what happens if taxi licenses are not given to composite traffic (they are based on estimated demand, while bus licenses are not). Table(Length_1,Zone,Vehicle_noch,Output_1,Scenario5,Period_1)( 0,0,0, 5072,472,255, 12.95K,1100,605, 25.56K,2345,1313, 39.18K,3409,1952, 0,0,0, 0,0,0, 279,0,0, 2313,27,0, 6579,54,0, 0,0,0, 12,0,0, 287.8,3,0, 2498,20.1,0, 7731,57.8,6, 0,0,0, 269,0,0, 647,0,0, 1040,0,0, 1248,0,0, 0,0,0, 0,0,0, 0.9,0,0, 9.1,0,0, 27.37,0,0, 0,0,0, 825,0,0, 2903,0,0, 6597,0,0, 9924,0,0, 'NAN','NAN','NAN', 5.484,5.916,5.995, 4.405,5.623,5.952, 3.383,4.988,5.812, 2.838,4.438,5.609, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 8019,270,0, 32.87K,1476,234, 75.61K,5013,1269, 114.3K,8577,2682, 0,0,0, 8704,224.1,13, 41.91K,1625,182.3, 105.4K,6098,1019, 167.5K,11.53K,2540, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 22.53,0,0, 116.6,0,0, 278.8,0,0, 445.8,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 'NAN','NAN','NAN', 10.97,11.83,11.99, 8.81,11.25,11.9, 6.766,9.976,11.62, 5.677,8.877,11.22, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 3100,0,0, 3000,0,0, 2878,0,0, 2596,0,0, 'NAN','NAN','NAN', 5.484,5.916,5.995, 4.405,5.623,5.952, 3.383,4.988,5.812, 2.838,4.438,5.609, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 'NAN','NAN','NAN', 10.97,11.83,11.99, 8.81,11.25,11.9, 6.766,9.976,11.62, 5.677,8.877,11.22, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 12.66K,1656,1016, 18.93K,3164,2231, 22.93K,4586,3871, 24.97K,5381,4996, 0,0,0, 79.07K,13.38K,10.15K, 102K,20.28K,18.46K, 115.1K,26K,26.84K, 120.2K,28.45K,31.59K, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 169.9,0,0, 203.7,0,0, 221.5,0,0, 224.2,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 'NAN','NAN','NAN', 10.97,11.83,11.99, 8.81,11.25,11.9, 6.766,9.976,11.62, 5.677,8.877,11.22, 52.02K,4467,2529, 46.44K,4073,2323, 38.6K,3382,1937, 25.99K,2198,1296, 12.58K,1175,607, 52.02K,4467,2529, 46.44K,4073,2323, 38.6K,3382,1937, 25.99K,2198,1296, 12.58K,1175,607, 739.6K,72.94K,43.96K, 672.6K,66.2K,39.19K, 573K,54.91K,32.92K, 397.1K,38.57K,22.17K, 212.5K,20.1K,11.14K, 7295,0,0, 6506,0,0, 5229,0,0, 3697,0,0, 2160,0,0, 5093,0,0, 4604,0,0, 3894,0,0, 2720,0,0, 1435,0,0, 68.85K,0,0, 63.2K,0,0, 53.53K,0,0, 37.42K,0,0, 21.54K,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 7312,648,389, 18.36K,1613,895, 36.19K,3115,1782, 54.72K,4817,2697, 0,0,0, 0,0,0, 54,0,0, 1026,0,0, 4023,9,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 704,0,0, 1030,0,0, 1508,0,0, 2040,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 2727,36,0, 14.51K,621,18, 37.79K,2151,243, 61.4K,4203,693, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 9722,1161,571, 16.72K,2288,1442, 22.62K,3598,2708, 25.95K,4630,3638, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 72.97K,6301,3671, 65.32K,5777,3202, 55.33K,4719,2632, 36.42K,3218,1741, 18.26K,1586,899, 72.97K,6301,3671, 65.32K,5777,3202, 55.33K,4719,2632, 36.42K,3218,1741, 18.26K,1586,899, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 14.27K,0,0, 13.53K,0,0, 11.18K,0,0, 8109,0,0, 4419,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 14.87K,1318,754, 37.25K,3214,1901, 74.65K,6542,3559, 111.4K,9799,5401, 0,0,0, 36,0,0, 531,9,0, 3879,36,0, 11.3K,117,18, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 4399,0,0, 5916,0,0, 8783,0,0, 11.67K,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 16.34K,414,36, 80.14K,2979,333, 203.4K,11.49K,1773, 324.4K,22.19K,4689, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 43.74K,5201,2959, 69.25K,10.8K,6890, 86.39K,16.21K,12.54K, 95.12K,19.03K,16.81K, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 149K,13.16K,7363, 134.6K,11.83K,6523, 111.9K,9623,5510, 74.28K,6616,3646, 37.74K,3330,1806, 149K,13.16K,7363, 134.6K,11.83K,6523, 111.9K,9623,5510, 74.28K,6616,3646, 37.74K,3330,1806, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 71.65K,0,0, 64.96K,0,0, 55.9K,0,0, 39.45K,0,0, 23.42K,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 1412,120,65, 3522,307,172, 6951,675,353, 10.49K,890,506, 0,0,0, 0,0,0, 9,0,0, 405,0,0, 1134,9,0, 0,0,0, 0,0,0, 1113,0,0, 12.79K,28.3,0, 43.51K,166.1,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 'NAN','NAN','NAN', 5.69,5.976,5.997, 4.833,5.773,5.988, 3.84,5.295,5.917, 3.269,4.854,5.789, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 45,0,0, 828,0,0, 3177,63,18, 5724,189,18, 0,0,0, 23.25K,260.8,13.4, 137.5K,4587,170.4, 377.3K,19.21K,2004, 623.1K,38.4K,6265, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 'NAN','NAN','NAN', 11.38,11.95,11.99, 9.666,11.55,11.98, 7.68,10.59,11.83, 6.538,9.708,11.58, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 'NAN','NAN','NAN', 5.69,5.976,5.997, 4.833,5.773,5.988, 3.84,5.295,5.917, 3.269,4.854,5.789, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 'NAN','NAN','NAN', 11.38,11.95,11.99, 9.666,11.55,11.98, 7.68,10.59,11.83, 6.538,9.708,11.58, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 1414,136,68, 2997,324,193, 4272,582,380, 5038,807,513, 0,0,0, 386.3K,62.09K,41.91K, 526.1K,99.57K,84.6K, 611.1K,131K,126.9K, 650.8K,148.2K,154.1K, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 'NAN','NAN','NAN', 11.38,11.95,11.99, 9.666,11.55,11.98, 7.68,10.59,11.83, 6.538,9.708,11.58, 13.98K,1212,670, 12.62K,1125,656, 10.63K,903,531, 6955,645,322, 3571,337,183, 13.98K,1212,670, 12.62K,1125,656, 10.63K,903,531, 6955,645,322, 3571,337,183, 8.498M,820.4K,478.1K, 7.698M,747.1K,427.2K, 6.53M,622.8K,361.7K, 4.502M,423.9K,242.9K, 2.371M,215.9K,121.9K, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 12.41K,1111,547, 30.96K,2679,1481, 61.98K,5316,3085, 92.74K,8117,4460, 0,0,0, 0,0,0, 90,0,0, 2547,9,0, 8271,18,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 4221,36,0, 21.02K,828,18, 49.89K,3033,324, 75.28K,5877,963, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 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71.06K,6303,3487, 178.6K,15.63K,8690, 354.8K,31.28K,17.36K, 534.1K,46.74K,25.99K, 0,0,0, 0,0,0, 882,0,0, 9234,18,0, 30.84K,153,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 21.67K,279,18, 123.3K,4194,189, 338.5K,17.3K,2016, 558.4K,34.25K,6120, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 88K,9955,5134, 151K,21.3K,12.93K, 195.7K,33.75K,23.97K, 219.5K,42.16K,33.11K, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 711.7K,62.58K,34.77K, 638.8K,56.31K,31.03K, 532.4K,46.52K,26.14K, 355.2K,31.11K,17.39K, 178K,15.58K,8627, 711.7K,62.58K,34.77K, 638.8K,56.31K,31.03K, 532.4K,46.52K,26.14K, 355.2K,31.11K,17.39K, 178K,15.58K,8627, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0, 0,0,0 ) 296,368,1 48,24 2,102,90,476,332 2,559,326,416,303,0,MIDM [Scenario5,Period_1] concat(Scenarios1_0_3,Scenarios1_0_2,Scenario1_0_1,Scenario102,Scenario_1) 296,152,1 48,24 [Scenario_1,Input_var] A table of different scenarios to be studied. Each row contains the values for the input variables used for the scenario. Table(Input_var,Scenario1_0_1)( 0,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1,0.02,0.05,0.1,0.25,0.4,0.45,0.5,0.55,0.65,0.75,0.9,1, 7,7,7,7,7,7,7,7,7,7,7,7,7,6,6,6,6,6,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,4,4,4,4,4,4,4,4,4,4,4,4,3,3,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 ) ['Composite fraction','Guarantee level','Lim'] 64,152,1 48,24 2,394,67,698,560,0,MIDM 2,430,154,675,554,0,MIDM 52425,39321,65535 [Input_var,Scenario] [Input_var,Scenario1_0_1] Scen[.scenario=Scenario_1, zone=hma_zone] 448,368,1 48,24 [Scenario_1,Output_1] [Length_1,1,Vehicle_noch,1,Period_1,1,Hma_zone,1,Output_1,1,Scenario_1,1] ['Downtown','Centre','Suburb'] 448,400,1 48,12 1 56,88,1 48,24 1,0,0,1,1,1,0,,0, Cost_elements 1 208,72,1 68,55 1,1,1,1,1,1,0,,1, Costs_not_included__ 1 472,56,1 52,48 65535,65532,19661 Composite_traffic_is 1 688,96,1 52,48 65535,65532,19661 Total_societal__voi_ 0 104,256,1 96,12 Under operation: CyberCab: The CyberCab is a new people mover system which first application is a temporary installation during the Floriade 2002 Ð a horticultural exposition organized once every ten years. 25 CyberCabs will provide transportation to the summit of the 40 meter high observation point: Big SpottersÕ Hill. The CyberCab is a fully automated vehicle seating 4 passengers. The system is operated by 2getthere.<a href="http://faculty.washington.edu/jbs/itrans/cybercab.htm">Click</a> Under planning: HiLoMag: special high-speed gateways for dual-mode cars <a href="http://faculty.washington.edu/~jbs/itrans/hilo1.htm">Click</a> BiWay dual mode transport: network of elevated tracks along which vehicles are magnetically levitated, and guided under computer control. <a href="http://www.buick.co.uk/biway/intro4.html">Click</a> Other_actions 104,360,1 76,72 2,299,66,476,385 composite_traffic_dummy 504,264,1 76,56 There is an inefficiency bump at 0-30% (depending on various details about organising the traffic) composite fraction: with too low trip volumes, the benefits from aggregating trips are not realised, and the system is not profitable Inefficiency bump in Finnish: tehottomuustšyssy. composite_traffic_dummy 480,408,1 80,72 2,103,257,476,343 65535,65532,19661 In composite traffic, a centralised system collects the information on all trips online, aggregates the trips with the same origin and destination into public vehicles with eight or four seats, and sends the travel instructions to the passengers' mobile phones. composite_traffic_dummy 504,64,1 48,55 1,1,1,1,1,1,0,,1, [Alias Composite_traffic_a1] The pressures from road traffic have stimulated efforts to reduce emissions (electric, hybrid, and hydrogen cars1, natural gas buses2, catalysts and particle traps3, and driving style4); congestion (traffic control5, street tolls, public transport subsidies); injuries (anti-locking brakes, airbags, speed limits6); and need to travel (urban planning7). Emissions 328,336,1 72,52 2,310,109,596,455 1. Ortmeyer,T.H. & Pillay,P. Trends in transportation sector technology energy use and greenhouse gas emissions. Proceedings of the Ieee 89, 1837-1847 (2001). 2. Tainio,M. et al. Health effect caused by primary fine particulate matter (PM2.5) emitted from busses in Helsinki Metropolitan Area, Finland. Risk Anal. 25, 149-158 (2005). 3. Mediavilla-Sahagun,A. & ApSimon,H.M. Urban scale integrated assessment of options to reduce PM10 in London towards attainment of air quality objectives. Atmos. Environ. 37, 4651-4665 (2003). 4. Vangi,D. & Virga,A. Evaluation of energy-saving driving styles for bus drivers. Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering 217, 299-305 (2003). 5. Hounsell,N.B. & McDonald,M. Urban network traffic control. Proceedings of the Institution of Mechanical Engineers Part I-Journal of Systems and Control Engineering 215, 325-334 (2001). 6. Elvik,R. Optimal speed limits - Limits of optimality models. Transportation Research Record 32-38 (2002). 7. Banister,D. Reducing the need to travel. Environment and Planning B-Planning & Design 24, 437-449 (1997). 0 104,64,1 96,12 0 104,96,1 96,12 0 104,128,1 96,12 0 104,160,1 96,12 0 104,32,1 96,12 0 104,192,1 96,12 0 104,224,1 96,12 New_fuels_and_engine; Particle_traps__cata; Driving_style; Traffic_control__spe; Urban_planning; Composite_traffic_du 328,64,1 88,12 Driving_style; Traffic_control__spe; Subsidies_to_public_; Urban_planning; Composite_traffic_du 328,128,1 88,12 Traffic_control__spe; Airbags__abs_brakes; Composite_traffic_du; Driving_style 328,160,1 88,12 Urban_planning 328,256,1 88,12 Urban_planning; Composite_traffic_du 328,192,1 88,12 Subsidies_to_public_; Composite_traffic_du 328,32,1 88,12 Urban_planning; Composite_traffic_du 328,224,1 88,12 jtue 15. Aprta 2005 14:34 48,24 56,208,1 48,24 1,0,1,1,1,1,0,,0, 1,40,0,125,398,17 Other_actions; Effect; The_marginal_cost_of 320,240,1 72,72 65535,31131,19661 State 432,96,1 88,60 Only a few studies have estimated the full chain from control techniques to health effects. Our previous work and a few others have compared the effects of emissions control techniques to health in full-scale risk assessment or cost-benefit analysis. The conclusion has been that the reduction of emissions has significant public health benefits. There are risk assessment studies on various technologies, but they are using emission-per-car models. Measures to reduce kilometres driven by car have not been much studied. It would be interesting to compare these results to studies with control technologies. Pressure 248,80,1 76,64 ARVO Henkilškohtaiset ja haltijakohtaiset matkakortit Kaikilla arvolipuilla voi vaihtaa lipun voimassaoloaikana. Liput ovat voimassa ¥ Helsingin sisŠisillŠ matkoilla 60 minuuttia ¥ seutumatkoilla sekŠ Espoon, Kauniaisten ja Vantaan sisŠisillŠ matkoilla 80 minuuttia. SEUTU Aikuinen Lapsi ¥ arvolippu 2,90 ­ 1,45 ­ ¥ pŠivŠarvolippu ma-pe 9-14 2,70 ­ ¥ yšarvolippu ma-su 2-4.30 4,00 ­ HELSINGIN SIS€INEN Aikuinen Lapsi ¥ arvolippu 1,70 ­ 0,70 ­ ¥ pŠivŠarvolippu ma-pe 9-14 1,40 ­ ¥ yšarvolippu ma-su 2-4.30 2,50 ­ ¥ arvolippu, raitiovaunu 1,28 ­ ________________________________ Matkakorttiyksikšn toimintamenot vuonna 2005 ovat noin 4,2 milj. euroa, mikŠ on hieman vŠhemmŠn kuin edellisvuonna. __________________________________ YTV:n matkakorttijŠrjestelmŠn piirissŠ on 800.000 matkakortin kŠyttŠjŠŠ pŠŠkaupunkiseudulla. PŠivittŠin jŠrjestelmŠŠ kŠytetŠŠn yli miljoonan matkan tekemiseen. 4.2M/1M/365 64,136,1 48,24 2,325,106,476,384 65535,52427,65534 <a href="http://www.ytv.fi/matkakortti/mitamaksaa.html">Ticket prices (in Finnish)</a> <a href="http://www.ytv.fi/yleis/asiak/poutakirjat/04015347.HTM"> Total costs of the travel card system (matkakortti)</a> <a href="http://www.ytv.fi/liikenne/ajank/uutinen.php?id=2774">Total trip volumes using travel card</a> The major advantage of a personal car is that you can drive it from your starting point directly to your destination according to your own time schedule. This public transportation model does the same. When a large enough population moves around, its transportation needs can be met even if a part of it is not known until, let's say, five minutes beforehand. The system is based on assumptions that a vast majority of trips can be predicted based on statistics and on consumers' early orders, and that it is possible to organise the public vehicles, the 'buses' and 'scooters' to fulfill the needs and run optimally at the same time. 0 120,224,1 64,60 We wish to thank Prof. Matti Jantunen, M.Sc. Olli Leino, M.Sc. Marjo Niittynen, M.Sc. Sanna Lensu, and Ms. Arja Tamminen for their helpful comments, evaluations, and work to collect data. The idea of a transportation system with public vehicles running without predetermined route was independently and interdepently developed by Matti Jantunen and Jouni Tuomisto. Jouni T. Tuomisto invented the idea of the work and performed most of the modelling. The first drafts of the model were written in November 2002 by Jouni Tuomisto. Marko Tainio reviewed the literature, checked the model, and wrote the first draft of the paper. This study was funded by the Academy of Finland, grant 53307, and the National Technology Agency of Finland (Tekes), grant 40715/01. 0 472,200,1 48,24 2,102,90,476,405 1 656,360,1 48,55 1,1,1,1,1,1,0,,1, Composite_traffic_ag 0 56,456,1 48,24 0 56,512,1 48,24 65535,65532,19661 0 56,400,1 48,24 0 jtue 15. Aprta 2005 14:34 56,344,1 48,29 Code legend for model items 64,277,-1 64,37 1,0,0,1,0,0,1,,0, 39321,52431,65535 Arial, 19 1 504,128,1 48,38 65535,65532,19661 A45_60__composite_fr DC-attribute with refinement Scheme (if any) Value Title Composite traffic model 1.0.1 Creator Tuomisto, Jouni Creator Tainio, Marko Subject Trip aggregation Subject Urban traffic Subject Public transportation Description.abstract Background Traffic congestion is rapidly becoming the most important obstacle to urban development. In addition, traffic creates major health, environmental, and economical problems. Nonetheless, automobiles are crucial for the functions of the modern society. Most proposals for sustainable traffic solutions face major political opposition, economical consequences, or technical problems. Methods We performed a decision analysis in a poorly studied area, trip aggregation, and studied decisions from the perspective of two different stakeholders, the passenger and society. We modelled the impact and potential of composite traffic, a hypothetical large-scale demand-responsive public transport system for the Helsinki metropolitan area, where a centralised system would collect the information on all trip demands online, would merge the trips with the same origin and destination into public vehicles with eight or four seats, and then would transmit the trip instructions to the passengers' mobile phones. Results We show here that in an urban area with one million inhabitants, trip aggregation could reduce the health, environmental, and other detrimental impacts of car traffic typically by 50-70 %, and if implemented could attract about half of the car passengers, and within a broad operational range would require no public subsidies. Conclusions Composite traffic provides new degrees of freedom in urban decision-making in identifying novel solutions to the problems of urban traffic. Publisher Kansanterveyslaitos (KTL; National Public Health Institute) Date.issued W3C-DTF 2005-11-30 Type DCMIType Software Format IMT text/xml Format.medium computerFile Format 836 kB Identifier http://www.ktl.fi/risk Identifier URN URN:NBN:fi-fe20051439 Language ISO639-2 en Rights Copyright Kansanterveyslaitos, 2005 0 88,28,1 80,20 2,105,198,476,499 65535,54067,19661 other_actions 504,400,1 72,56 ktluser 12. heita 2005 22:51 48,24 56,85,1 48,29 1,0,0,1,1,1,0,,0, 1,23,54,604,563,17 0 172,44,1 156,12 1,0,0,1,0,0,0,72,0,1 Choose_comp 0 172,68,1 156,12 1,0,0,1,0,0,0,72,0,1 Choose_guar 0 172,92,1 156,12 1,0,0,1,0,0,0,105,0,1 Choose_period 1 172,196,1 156,12 1,0,0,1,0,0,0,72,0,1 Table_1_pressures 1 172,292,1 156,12 1,0,0,1,0,0,0,72,0,1 Fig_5a_societal_cost 1 172,316,1 156,12 1,0,0,1,0,0,0,72,0,1 Fig_5b_subsidies 1 172,340,1 156,12 1,0,0,1,0,0,0,70,0,1 Fig_5c_expanding 1 172,268,1 156,12 1,0,0,1,0,0,0,72,0,1 Fig_4_cost_variation 1 172,220,1 156,12 1,0,0,1,0,0,0,72,0,1 Fig_2_trips 1 172,244,1 156,12 1,0,0,1,0,0,0,72,0,1 Fig_3_cost_by_source 1 172,364,1 156,12 1,0,0,1,0,0,0,72,0,1 Fig_6a_passenger_voi 1 172,388,1 156,12 1,0,0,1,0,0,0,72,0,1 Fig_6b_societal_voi 0 172,20,1 156,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Choose_scen 0 172,164,1 156,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Choose_flexible 0 172,140,1 156,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Choose_large 0 172,116,1 156,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Choose_nochange 0 56,568,1 48,24 Version 1.0.4 23.1.2006 Jouni Tuomisto TŠnŠŠn yhdistettiin eri skenaariot yleispŠtevŠksi tarkasteluksi siten, ettŠ Choose_scen-solmusta vain valitaan, mikŠ skenaario(joukko) otetaan tarkasteluun, ja kaikki asianmukaiset datasolmut valikoituvat kŠyttššn. TŠstŠ on yksi poikkeus: Fig 2 Trips, jota ei ole pŠivitetty. SiitŠ ei myšskŠŠn ole laskettu kaikki samoja skenaarioita kuin muista outputeista, joten sekin pitŠisi tehdŠ. Imuroitiin malliin myšs se skenaario (29.8.2005) jossa oli katsottu 9-paikkaisten autojen tilanteessa tuloksia. Version 1.0.5 28.7.2008 Jouni Tuomisto This version has the same data, scenarios, and traffic modelling as version 1.0.4. However, index names have been changed so that they are compatible with the result database, and a separate module about database connection has been added. Version 1.0.6 16.9.2008 Jouni Tuomisto A new version of RDB_connection.ANA was added. Now it works fine with the updated result database structure (15.9.2008). This is hopefully the last sub-version of 1.0.X series. This is only used to play with the published results (Tuomisto and Tainio 2005 BMC Public Health). All new results will be calculated with versions 1.8. and newer. 0 232,24,1 48,12 2,771,113,476,467 65535,54067,19661 HP_Omistaja 9. maata 2008 10:42 jtue 16. syyta 2008 10:11 48,24 656,72,0 48,29 1,0,0,1,1,1,0,0,0,0 1,10,6,529,644,17 Arial, 15 100,1,1,1,1,9,2970,2100,1,0 0 156,400,1 140,16 1,0,0,1,0,0,0,78,0,1 52425,39321,65535 New_dimension_info 0 156,432,1 140,16 1,0,0,1,0,0,0,78,0,1 52425,39321,65535 Run_info 0 156,336,1 140,16 1,0,0,1,0,0,0,78,0,1 52425,39321,65535 New_variable_info 0 156,256,1 140,16 1,0,0,1,0,0,0,78,0,1 52425,39321,65535 Variables_to_be_save jtue 18. heita 2008 10:14 48,24 408,512,1 48,24 1,663,9,511,692,17 This library contains functions to make various instances of concatenation more convenient. Concat3 thru Concat10 are generalizations of the built-in Concat function which concatenate from 3 to 10 arrays in a single call (while the built-in Concat concatenates two arrays). ConcatRows concatenates all the rows of a single array. David Kendall & Lonnie Chrisman Mon, Jan 26, 2004 8:49 AM Lonnie Wed, Sep 05, 2007 3:23 PM 48,24 288,616,1 68,20 1,0,0,1,1,1,0,0,0,0 1,39,36,798,452,23 (A1, A2, A3: ArrayType; I1, I2, I3, J: IndexType ) Concatenates three arrays, A1, A2, and A3. I1, I2, and I3 are the indexes that are joined; J is the index of the new array; J usually is the concatenation of I1, I2, and I3 Index I12 := Concat(I1,I2); Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, J ) 88,64,1 48,26 2,56,56,986,596 A1,A2,A3,I1,I2,I3,J (A1, A2, A3, A4: ArrayType; I1, I2, I3, I4, J: IndexType ) Concatenates four arrays, A1, A2, A3, and A4. I1, I2, I3, and I4 are the indexes that are joined; J is the index of the new array; J usually is the concatenation of I1, I2, I3, and I4. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, J); 192,64,1 48,24 2,30,30,986,596 A1,A2,A3,A4,I1,I2,I3,I4,J 0 (A1, A2, A3, A4, A5, A6, A7, A8, A9: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, I9, J: IndexType) Concatenates nine arrays, A1, ..., A9. I1, ..., I9 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I9. Index I12 := Concat(I1,I2); Index I123 := Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Index I1234567 := Concat(I123456, I7); Index I12345678 := Concat(I1234567, I8); Concat( Concat( Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, I1234567), A8, I1234567, I8, I12345678), A9, I12345678, I9, J); 88,232,1 48,24 2,27,120,469,638 A1,A2,A3,A4,A5,A6,A7,A8,A9,I1,I2,I3,I4,I5,I6,I7,I8,I9,J 0 (A1, A2, A3, A4, A5: ArrayType; I1, I2, I3, I4, I5, J: IndexType ) Concatenates five arrays, A1, ..., A5. I1, ..., I5 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I5. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, J); 88,120,1 48,24 2,160,160,986,596 A1,A2,A3,A4,A5,I1,I2,I3,I4,I5,J (A1, A2, A3, A4, A5, A6: ArrayType; I1, I2, I3, I4, I5, I6, J: IndexType ) Concatenates six arrays, A1, ..., A6. I1, ..., I6 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I6. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, J); 192,120,1 48,24 2,644,94,602,712 A1,A2,A3,A4,A5,A6,I1,I2,I3,I4,I5,I6,J 0 (A1, A2, A3, A4, A5, A6, A7: ArrayType; I1, I2, I3, I4, I5, I6, I7, J: IndexType ) Concatenates seven arrays, A1, ..., A7. I1, ..., I7 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I7. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, J); 88,176,1 48,24 2,580,98,551,565 A1,A2,A3,A4,A5,A6,A7,I1,I2,I3,I4,I5,I6,I7,J (A1, A2, A3, A4, A5, A6, A7, A8: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, J: IndexType ) Concatenates eight arrays, A1, ..., A8. I1, ..., I8 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I8. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Index I1234567 := Concat(I123456, I7); Concat( Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, I1234567), A8, I1234567, I8, J); 192,176,1 48,24 2,12,98,561,737 A1,A2,A3,A4,A5,A6,A7,A8,I1,I2,I3,I4,I5,I6,I7,I8,J 0 (A1, A2, A3, A4, A5, A6, A7, A8, A9, A10: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, I9, I10, J: IndexType) Concatenates ten arrays, A1, ..., A10. I1, ..., I10 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I10. Index I12 := Concat(I1,I2); Index I123 := Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Index I1234567 := Concat(I123456, I7); Index I12345678 := Concat(I1234567, I8); Index I123456789 := Concat(I12345678, I9); Concat( Concat( Concat( Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, I1234567), A8, I1234567, I8, I12345678), A9, I12345678, I9, I123456789), A10, I123456789, I10, J); 192,232,1 48,24 2,542,93,632,744 A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,I1,I2,I3,I4,I5,I6,I7,I8,I9,I10,J 0 (A : ArrayType ; RowIndex,ColIndex,ResultIndex : IndexType) Takes an array, A indexed by RowIndex & ColIndex, and concatenates each row, henceforth flattening the array by one dimension. The result is indexed by ResultIndex, which must be an index with size(RowIndex) * size(ColIndex) elements. index L := [ identifier of RowIndex, identifier of ColIndex, "val"]; slice(Mdarraytotable(A,ResultIndex,L),L,3) 320,64,1 64,24 2,30,320,478,348 A,RowIndex,ColIndex,ResultIndex Table(Self,Vari)( 'Fig_3_cost_by_source',1, 'Fig_5a_societal_cost',1, 'Fig_5b_subsidies',0, 'Fig_5c_expanding',1 ) ['item 1','item 2','item 3','item 4'] 56,393,1 48,22 1,1,1,1,1,1,0,0,0,0 2,102,90,476,481 2,836,351,416,482,0,MIDM 2,663,308,420,375,0,MIDM [Formnode Var_for_rdb1] 52425,39321,65535 [Vari,Self] [Vari,Self] [1,1,1,0] [Sys_localindex('I'),1,Sys_localindex('F'),1,Sys_localindex('G'),1] 1) The process is done for each variable one at a time (this is indexed by x). 2) The variable is given index runn which is equal to run if probabilistic and 0 if not. 3) The array is flattened first to 2-D, the value only is kept, added with sample and result_id. 4) Variables are concatenated to each other. 5) Index i is made the index of the implicit index. var output:= 0; var e:= card_res; index j:= ['Result_id','Result','Sample']; var x:= 1; while x<= size(Variables_to_be_save)/size(vari) do ( var c:= slice(Variables_to_be_save,Variables_to_be_save,x); index runn:= if c[Vari='Probabilistic?']=1 then copyindex(run) else [0]; var b:= getfract(mid(evaluate(c[vari='Var_name'])),0.5); var a:= if c[Vari='Probabilistic?']=1 then ( var d:= sample(evaluate(c[vari='Var_name'])); d[run=runn]) else ( var d:= b; if runn=0 then d else d); index endscen:= 1+e..size(b)+e; index row2:= 1..size(endscen)*size(runn); index l:= concat(indexnames(b),['Value']); a:= mdarraytotable(a,endscen,l); a:= a[.l='Value']; a:= array(j,[endscen,a,runn]); a:= concatrows(a,endscen,runn,row2); output:= if x= 1 then a else for y:= j do ( concat(output[j=y],a[j=y]) ); e:= e+size(b); x:= x+1); index i:= 1..size(output)/size(j); for y:= j do (slice(output[j=y],i)) 288,312,1 48,16 2,31,17,528,769 2,624,21,366,606,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] [0,0,0,0] [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24] [Sys_localindex('RUNN'),1,Iresult,3,Sys_localindex('ENDSCEN'),1] if slice(indices_missing,1) = 'All indices have been defined already' then inp_ind else ( dbwrite(odbc_write, appendtablesql(inp_ind,Indices_missing,Inp_ind.j,'`Index` ')); 'Done') 408,80,1 48,16 65535,45873,39321 [Indices_missing,Sys_localindex('J')] [] Lonnie Thu, Sep 11, 1997 2:15 PM Lonnie Tue, Feb 05, 2008 10:03 AM 48,24 408,616,1 52,20 1,1,1,1,1,1,0,0,0,0 1,20,272,499,497,17 Arial, 13 (A:ArrayType;I:IndexType;L:IndexType;row;dbTableName) Generates the SQL "INSERT INTO" statement for one line of table A. A is a 2-D table indexed by rows I and columns L. L's domain serves as the column names in the database table. dbTableName is the name of the table in the database. The result begins with two semi-colons, since it will be used with an SQL statement preceeding it. 29.8.2008 Jouni Tuomisto I added the parameter IGNORE because it ignores rows that would cause duplicate-key violations. This way, there is no need to check for e.g. existing locations of new indices. (';;INSERT IGNORE INTO ' & dbTableName & '(' & JoinText(L,L,',') & ') VALUES (' & Vallist(A[I=row],L)) & ')' 184,32,1 52,24 2,41,136,487,469 A,I,L,row,dbTableName (V:ArrayType;I:IndexType) Takes a list of values, and returns a string which the concatenation of each value, separated by commas, and with each value quoted. JoinText( '''' & V & '''', I, ',') 72,32,0 52,24 V,I 1,F,4,14,0,0 (Tabl:ArrayType;RowIndex:IndexType;LabelIndex:IndexType;dbTableName) Returns the SQL that will write the table to the database table. This can be used as the second argument to DBWrite. This SQL statement replaces the entire contents of an existing table with the new data. 'DELETE FROM '& Dbtablename & JoinText(Insertrecsql(Tabl, Rowindex, Labelindex, Rowindex, Dbtablename),RowIndex) 328,32,1 88,24 2,728,341,510,476 Tabl,RowIndex,LabelIndex,dbTableName (Tabl:ArrayType;RowIndex:IndexType;LabelIndex:IndexType;dbTableName) Returns the SQL that will write the table to the database table. This can be used as the second argument to DBWrite. This SQL statement replaces the entire contents of an existing table with the new data. JoinText(Insertrecsql(Tabl, Rowindex, Labelindex, Rowindex, Dbtablename),RowIndex) 328,88,1 88,24 2,559,127,510,476 Tabl,RowIndex,LabelIndex,dbTableName var a:= if Dimensions[.j='Dim_name']=New_index_info[iind='Dim_name'] then Dimensions[.j='Dim_id'] else 0; a:= sum(a,dimensions.i); index j:= ['Ind_id','Ind_name','Dim_id']; array(j,[ Card_ind+@Indices_missing, indices_missing, a]) ['item 1'] 288,80,1 48,16 2,197,430,416,303,0,MIDM 2,616,387,416,303,0,MIDM 19661,48336,65535 [Iind,Self] [Indices_missing,Sys_localindex('J')] ['item 1'] [Indices_missing,1,Sys_localindex('CO'),1,Sys_localindex('I'),1] ['Dim_name'] 56,192,1 48,13 Table(Idim,Dimensions_missing)( 0, 0 ) ['item 1'] 56,41,1 48,31 2,197,253,767,303,0,MIDM 2,297,603,561,204,0,MIDM [Formnode Inp_dim1] 52425,39321,65535 [Idim,Dimensions_missing] [Idim,Dimensions_missing] ['item 1'] ['Dim_title','Dim_unit'] 56,80,1 48,13 ['Dim_title','Dim_unit'] var a:= if New_locs[.j='Ind_name'] = Inp_ind[.j='Ind_name'] then Inp_ind[.j='Ind_id'] else 0; a:= sum(a,Indices_missing); index j:= ['Ind_id','Row_number','Loc_id']; a:= array(j,[a,New_locs[.j='Row_number'],inp_loc[.j='Loc_id']]) ['item 1'] 288,160,1 48,16 2,597,410,598,363 2,250,441,416,303,0,MIDM 2,782,442,448,347,0,MIDM 19661,48336,65535 [Self] [Sys_localindex('J'),Sys_localindex('I')] ['item 1'] 1) a defines the dim_id. 2) b defines the loc_id. If the location exists already, this loc_id is used; otherwise, a sequence of numbers is used. 3) information is written into the right table format. var a:= if New_locs[.j='Dim_name'] = Dimensions[.j='Dim_name'] then Dimensions[.j='Dim_id'] else 0; a:= max(a,Dimensions.i); var b:= if new_locs[.j='Dim_name'] = locations[.j='Dim_name'] and new_locs[.j='Location'] = locations[.j='Location'] then locations[.j='Loc_id'] else 0; b:= max(b,locations.i); b:= if b>0 then b else card_loc+@New_locs.i; index j:= ['Loc_id','Dim_id','Location']; a:= array(j,[b, a, New_locs[.j='Location']]); ['item 1','item 2','item 3','item 4','item 5','item 6','item 7','item 8','item 9','item 10','item 11','item 12','item 13','item 14','item 15','item 16','item 17','item 18','item 19','item 20','item 21'] 288,120,1 48,16 2,102,90,476,321 2,319,169,421,433,0,MIDM 2,18,370,563,302,0,MIDM 19661,48336,65535 [Self] [Sys_localindex('J'),Sys_localindex('I')] ['item 1','item 2','item 3','item 4','item 5','item 6','item 7','item 8','item 9','item 10','item 11','item 12','item 13','item 14','item 15','item 16','item 17','item 18','item 19','item 20','item 21'] [Sys_localindex('J'),3,Sys_localindex('K'),1] Reset; index a:= DBquery(odbc,'SELECT Ind_id FROM `Index` '); index b:= ['Ind_id']; max(max(DBTable(a, b ),a),b) 168,80,1 48,16 39325,65535,39321 Reset; index a:= DBquery(odbc,'SELECT Dim_id FROM `Dimension` '); index b:= ['Dim_id']; max(max(DBTable(a, b ),a),b) 168,40,1 48,16 39325,65535,39321 Reset; index a:= DBquery('DSN=resultdatabase','SELECT Loc_id FROM `Location` '); index b:= ['Loc_id']; max(max(DBTable(a, b ),a),b) 168,120,1 48,16 39325,65535,39321 if slice(dimensions_missing,1) = 'All dimensions have been defined already' then inp_dim else ( dbwrite(odbc_write, appendtablesql(Inp_dim,Dimensions_missing,Inp_dim.j,'Dimension ')); 'Done') 408,40,1 48,16 2,312,322,698,303,0,MIDM 65535,45873,39321 [Dimensions_missing,Sys_localindex('J')] [] Reset; index a:= DBquery(odbc,'SELECT Result_id FROM `Result` '); index b:= ['Result_id']; max(max(DBTable(a, b ),a),b) 168,312,1 48,16 39325,65535,39321 getfract forces the effect of sample() away, while mid doesn't 1) Does the process for each variable one at a time. Only the deterministic information about variables are considered (therefore getfract). 2) Gets the samplesize (0 if deterministic) for the variable. 3) Gets the Var_id for the variable. 4) Flattens the array. The value is discarded and only the location info is kept. Location is changed into text because that is the format in the RDB. 5) Gets the Dimension (Dim_id) for each index of h. 6) Gets the Location (Loc_id) for each cell in a, given the dimension and location. 7) Gets the Ind_id for each Index listed in h. 8) Flattens the array a. 9) Concatenates the results of all variables. 10) Makes Row the index of the implicit index. var output=0; var e:= card_res; index j:= ['Loc_id','Result_id','Var_id','Ind_id','N']; var x:= 1; while x<= size(Variables_to_be_save)/size(vari) do ( var c:= slice(Variables_to_be_save,Variables_to_be_save,x); var a:= getfract(mid(evaluate(c[vari='Var_name'])),0.5); Var n:= if c[vari='Probabilistic?']=0 then 0 else samplesize; var var_id:= if c[vari='Var_name'] = variables[.j='Var_name'] then variables[.j='Var_id'] else 0; var_id:= sum(var_id, variables.i); index h:= indexnames(a); index l:= concat(h,['Value']); index roww:= 1..size(a)*size(h); index endscen:= 1+e..size(a)+e; e:= e+size(a); a:= mdarraytotable(a,endscen,l); a:= a[l=h]&''; var dim:= if h=indices[.j='Ind_name'] then indices[.j='Dim_id'] else 0; dim:= sum(dim,indices.i); a:= if a=Locations[.j='Location'] and dim=Locations[.j='Dim_id'] then Locations[.j='Loc_id'] else 0; a:= sum(a,Locations.i); var ind_id:= if h= indices[.j='Ind_name'] then indices[.j='Ind_id'] else 0; ind_id:= sum(ind_id,indices.i); a:= array(j,[a,endscen,var_id,ind_id,n]); a:= concatrows(a,a.h,endscen,roww); output:= if x= 1 then a else for y:= j do ( concat(output[j=y],a[j=y]) ); x:= x+1); index i:= 1..size(output)/size(j); for y:= j do (slice(output[j=y],i)) 288,352,1 48,16 2,59,37,518,793 2,281,16,434,814,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] [] [Sys_localindex('H'),3,Sys_localindex('I'),1,Sys_localindex('ENDSCEN'),1] dbwrite(odbc_write, appendtablesql(inp_loc_of_result,inp_loc_of_result.i, inp_loc_of_result.j,'Loc_of_result ')); 'Done' 408,352,1 48,16 65535,45873,39321 [] var output:= 0; var e:= card_res; index j:= ['Result_id','Run_id']; var x:= 1; while x<= size(Variables_to_be_save)/size(vari) do ( var c:= slice(Variables_to_be_save,Variables_to_be_save,x); var b:= getfract(mid(evaluate(c[vari='Var_name'])),0.5); index endscen:= 1+e..size(b)+e; var a:= array(j,[endscen,card_run+1]); output:= if x= 1 then a else for y:= j do ( concat(output[j=y],a[j=y]) ); e:= e+size(b); x:= x+1); index i:= 1..size(output)/size(j); for y:= j do (slice(output[j=y],i)) 288,392,1 48,16 2,102,90,476,578 2,859,396,372,337,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] Reset; index a:= DBquery(odbc,'SELECT Run_id FROM `Run` '); index b:= ['Run_id']; max(max(DBTable(a, b ),a),b); 168,392,1 48,16 1,1,1,1,1,1,0,0,0,0 39325,65535,39321 Table(Irun)( '2008-09-16','Jouni',' CompositeTraffic_1_0_6.ANA 16.9.2008, RDB connection.ANA 16.9.2008') 168,464,1 48,16 2,246,120,629,303,0,MIDM 2,415,466,457,303,0,MIDM [Formnode Inp_run1] 52425,39321,65535 [Irun,Self] [Self,Irun] 2,D,4,2,0,0,4,0,$,0,"yyyy-mm-dd",0 ['item 1'] ['Run_date','Run_who','Run_method'] 168,488,1 48,13 ['Run_date','Run_who','Run_method'] Table(Ivar,Variables_missing)( 0, 0 ) 56,273,1 48,22 2,239,183,675,453 2,245,644,589,183,0,MIDM 2,152,434,886,303,0,MIDM [Formnode Inp_var1] 52425,39321,65535 [Ivar,Variables_missing] [Ivar,Variables_missing] 2,D,4,2,0,0,4,0,$,0,"yyyy-mm-dd",0 ['item 1','item 2'] ['Var_title','Var_unit'] 56,304,1 48,12 ['Var_title','Var_unit'] if slice(Variables_missing,1) = 'All variables have been defined already' then Inp_var else ( dbwrite(odbc_write, appendtablesql(Inp_var,Variables_missing,Inp_var.co,'Variable ')); 'Done') 408,272,1 48,16 65535,45873,39321 [Variables_missing,Sys_localindex('CO')] [] dbwrite(odbc_write, appendtablesql(Inp_run,Inp_run.i,Inp_run.j,'Run ')); 'Done' 408,432,1 48,16 65535,45873,39321 [] dbwrite(odbc_write, appendtablesql(inp_run_list,inp_run_list.i,inp_run_list.j,'Run_list ')); 'Done' 408,392,1 48,16 65535,45873,39321 [] dbwrite(odbc_write, appendtablesql(inp_result,inp_result.i,inp_result.j,'Result ')); 'Done' 408,312,1 48,16 65535,45873,39321 [] if slice(indices_missing,1) = 'All indices have been defined already' then inp_loc else ( dbwrite(odbc_write, appendtablesql(inp_loc, inp_loc.i, inp_loc.j, 'Location ')); 'Done') 408,120,1 48,16 65535,45873,39321 [Sys_localindex('I'),Sys_localindex('J')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [] if slice(indices_missing,1) = 'All indices have been defined already' then inp_row else ( dbwrite(odbc_write, appendtablesql(inp_row,New_locs.i,inp_row.j,'Rows ')); 'Done') 408,160,1 48,16 65535,45873,39321 [Sys_localindex('I'),Sys_localindex('J')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [] ['Var_name','Probabilistic?'] 56,424,1 48,12 ['Var_name','Probabilistic?'] 219 56,552,1 48,12 1,1,0,1,1,1,0,0,0,0 1) Takes the first index and calculates the parameters for it. 2) Does the same for all other indices and concatenates the parameter results. 3) All parameters are lumped into a single array. 4) A complicated way to take into account whether there are any new indices. This is done because if then else cannot be used for these arrays in a straightforward way (the condition cannot have critical dimensions). If-then-else is replaced with conditional slice. 5) Finally, extra rows are removed. var a:= indices_used; var b:= evaluate(a[@.indices_used=1]); var c:= if b=0 then a[@.indices_used=1] else a[@.indices_used=1]; var e:= 1..size(b); var x:= 2; while x<=size(a) do ( var d:= evaluate(a[@.indices_used=x]); b:= concat(b,d); c:= concat(c,(if d=0 then a[@.indices_used=x] else a[@.indices_used=x])); e:= concat(e,1..size(d)); x:= x+1); index k:= 1..size(b); b:= slice(b,k); c:= slice(c,k); e:= slice(e,k); var d:= New_index_info[iind='Dim_name',indices_missing=c]; d:= if d=null then 0 else d; index j:= ['Dim_name','Location','Ind_name','Row_number']; a:= array(j,[d,b&'',c,e]); index L:= 1..size(k)+1; index m:= ['All locations have been defined already']; a:= concat(a,m,k,m,L); index g:= if slice(indices_missing,1) = 'All indices have been defined already' then size(k)+1..size(k)+1 else k; a:= slice(a,L,g); a:= a[g=subset(a[.j='Dim_name'])]; index i:= 1..(size(a)/size(j)); for f:= j do slice(a[j=f],i) 168,160,1 48,16 2,7,40,476,692 2,545,129,651,303,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] ['','','','','','','','','',''] [Inp_ind,1,Indices_missing,1,Self,1] 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102;Database=resultdb;User=result_reader; Password=ora4ever;Option=3' 56,528,1 48,12 1,1,0,1,1,1,0,,0, 2,102,90,476,224 Reset; index i:= DBquery(odbc,'SELECT Dim_id, Dim_name FROM `Dimension` '); index j:= ['Dim_id','Dim_name']; DBTable(i, j ) 160,528,1 48,12 1,1,0,1,1,1,0,0,0,0 2,922,53,316,489,0,MIDM 39325,65535,39321 [Sys_localindex('J'),Sys_localindex('I')] This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. Var a is the variable name list that is being edited. Var e is the list of variables to be saved. Index k is a temporary index for a. The idea is to find all those variables listed in e (including "All variables have been defined already") that do NOT show up in the existing list of variables. var a:= Variables[.j='Var_name']; a:= ','&jointext(a,a.i,',')&','; var e:= Variables_to_be_save[vari='Var_name']; index k:= concat(['All variables have been defined already'],e); a:= if findintext(','&k&',',a)>0 then 0 else 1; a:= if sum(a,a.k)>1 and @k=1 then 0 else a; subset(a) 56,336,1 48,22 1,1,1,1,1,1,0,0,0,0 2,12,36,493,501 2,38,575,406,193,0,MIDM 39321,39325,65535 [Self,Variables_to_be_save] ['All variables have been defined already'] This node checks the indices listed in variables in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. For syntax help, see Variables_missing. var a:= Indices[.j='Ind_name']; a:= ','&jointext(a,a.i,',')&','; var e:= indices_used; index k:= concat(['All indices have been defined already'],e); a:= if findintext(','&k&',',a)>0 then 0 else 1; a:= if sum(a,a.k)>1 and @k=1 then 0 else a; subset(a) 56,224,1 48,22 1,1,1,1,1,1,0,0,0,0 2,531,26,493,499 2,526,562,490,264,0,MIDM 39321,39325,65535 [Self,Vari] ['All indices have been defined already'] Table(Iind,Indices_missing) 56,160,1 48,22 2,34,74,606,204,0,MIDM 2,32,590,520,238,0,MIDM [Formnode Input_ind1] 52425,39321,65535 [Iind,Indices_missing] [Iind,Indices_missing] This lists all indices that are used by all of the variables that are to be inserted into the result database. This is based on an assumption that the index Ind_var is always the outermost index and therefore last. Then it is sliced away. reset; var a:= Variables_to_be_save[vari='Var_name']; a:= indexnames(evaluate(a)); index i:= 1..size(a); a:= slice(a,i); a:= if a= 'Variables_to_be_save' then 0 else a; a[i=subset(a)] 168,200,1 48,16 2,796,247,476,566 2,216,232,416,303,0,MIDM [Sys_localindex('J'),Self] ['Age1','Municipality_fin1','Inp_var'] [Inp_ind,1,Indices_missing,1,Self,1] This node checks the indices listed in variables in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. For syntax help, see Variables_missing. reset; var a:= dimensions[.j='Dim_name']; a:= ','&jointext(a,a.i,',')&','; var e:= new_index_info[iind='Dim_name']&'' ; index k:= concat(['All dimensions have been defined already'],e); a:= if findintext(','&k&',',a)>0 then 0 else 1; a:= if sum(a,a.k)>1 and @k=1 then 0 else a; subset(a) 56,112,1 48,22 1,1,1,1,1,1,0,0,0,0 2,755,126,493,593 2,66,113,513,489,0,MIDM 39321,39325,65535 [Self,Indices_missing] ['All dimensions have been defined already'] var c:= Card_var; index co:= ['Var_id','Var_name','Var_title','Var_unit','Page_id','Wiki_id']; array(co,[ c+@Variables_missing, Variables_missing, New_variable_info[ivar='Var_title'], New_variable_info[ivar='Var_unit'], Selecttext( variables_missing, 2), Selecttext( variables_missing, 1,1)]) 288,272,1 48,16 1,1,1,1,1,1,0,0,0,0 2,102,90,502,425 2,255,386,800,303,0,MIDM 19661,48336,65535 [Variables_missing,Sys_localindex('CO')] Reset; index i:= DBquery(odbc,'SELECT Ind_id, Ind_name, Dimension.Dim_id, Dim_name FROM `Index`, Dimension WHERE `Index`.Dim_id=Dimension.Dim_id '); index j:= ['Ind_id','Ind_name','Dim_id','Dim_name']; DBTable(i, j ) 160,552,1 48,12 1,1,0,1,1,1,0,0,0,0 2,-73,97,476,296 2,740,152,481,493,0,MIDM 39325,65535,39321 [Sys_localindex('J'),Sys_localindex('I')] index j:= ['Dim_id','Dim_name','Dim_title','Dim_unit']; array(j,[ Card_dim+@Dimensions_missing, Dimensions_missing, New_dimension_info[Idim='Dim_title'], New_dimension_info[Idim='Dim_unit']]) 288,40,1 48,16 2,440,450,416,303,0,MIDM [Dimensions_missing,Sys_localindex('J')] 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102;Database=resultdb;User='&username&'; Password='&password&';Option=3' 56,624,1 48,12 1,1,0,1,1,1,0,,0, 'Add username' 56,576,0 48,12 1,1,1,1,1,1,0,0,0,0 [Formnode Username1] 52425,39321,65535 'Add password' 56,600,0 48,12 1,1,1,1,1,1,0,0,0,0 [Formnode Password1] 52425,39321,65535 index j:= ['Run_id','Run_date','Run_who','Run_method']; index i:= 1..1; var a:= Run_info[Irun=j]; a:= if j='Run_id' then Card_run+1 else a; a:= if j='Run_method' then a[j='Run_method']&', '&Run_data else a; array(i,[a]) 288,432,1 48,16 2,168,178,683,303,0,MIDM [Sys_localindex('I'),Sys_localindex('J')] Reset; index i:= DBquery(odbc,'SELECT Loc_id, Location, Dimension.Dim_id, Dim_name FROM `Location`, `Dimension` WHERE Location.Dim_id = Dimension.Dim_id '); index j:= ['Loc_id', 'Location', 'Dim_id', 'Dim_name']; DBTable(i, j) 160,576,1 48,12 1,1,0,1,1,1,0,0,0,0 2,102,90,476,445 2,572,44,696,521,0,MIDM 39325,65535,39321 [Sys_localindex('J'),Sys_localindex('I')] Reset; index a:= DBquery(odbc,'SELECT Var_id FROM `Variable` '); index b:= ['Var_id']; max(max(DBTable(a, b ),a),b) 168,272,1 48,16 1,1,1,1,1,1,0,0,0,0 2,376,386,416,303,0,MIDM 39325,65535,39321 This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. Reset; index i:= DBquery(odbc,'SELECT Var_id, Var_name FROM `Variable` '); index j:= ['Var_id','Var_name']; var a:= DBTable(i, j ); a 160,600,1 48,12 1,1,0,1,1,1,0,0,0,0 2,254,142,493,501 2,529,143,331,437,0,MIDM 39325,65535,39321 [Sys_localindex('J'),Sys_localindex('I')] ['H1991'] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] ['18-65','3'] 288,552,1 48,12 ['Harjavalta'] 280,576,1 64,12 2,192,588,476,224 Municipality_fin1&Age1 288,528,1 48,12 [Municipality_fin1,Age1] 'Edition: '&Analyticaedition&', Platform: '&Analyticaplatform&', Version: '&Analyticaversion&', Samplesize: '&Samplesize 168,432,1 48,16 2,119,357,476,303,0,MIDM 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 samplesize 400,560,1 48,16 [Formnode Size_of_sample1] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 (a:prob) index statistics:= ['Mean','SD','0.01','0.025','0.05','0.25','0.5 (Median)','0.75','0.95','0.975','0.99']; a:= array(statistics,[mean(a), sdeviation(a), getfract(a,0.01), getfract(a,0.025), getfract(a,0.05), getfract(a,0.25), getfract(a,0.5), getfract(a,0.75), getfract(a,0.95), getfract(a,0.975), getfract(a,0.99)]); round(a*10)/10&'' 400,528,1 48,12 a This module saves model results into the Result Database. You need a password for that. Note that the necessary variable, index, dimension, and run information will be asked. You must add all tables before the process is completed. 236,40,-1 236,40 Note! You can insert several variables at the same time. Each variable MUST have at least one index. 152,120,-1 144,32 Fill in the data below if needed (in this order). 168,352,-5 160,192 1,0,0,1,0,1,0,,0, 220,492,1 52,12 2,569,446,476,337 224,294,1 48,22 0 156,196,1 140,12 1,0,0,1,0,0,0,110,0,1 52425,39321,65535 Username 0 156,220,1 140,12 1,0,0,1,0,0,0,110,0,1 52425,39321,65535 Password 0 156,368,1 140,16 1,0,0,1,0,0,0,78,0,1 52425,39321,65535 New_index_info 1 156,460,1 140,12 1,0,0,1,0,0,0,72,0,1 Size_of_sample 220,516,1 52,12 2,569,446,476,337