Modelling road transport in Helsinki and Hague

The text on this page is taken from an equivalent page of the IEHIAS-project.

As part of the EU-funded INTARESE project, which contributed to the development of this Toolbox, a series of case studies was carried out to assess health impacts of different road transport policies in different cities, including the Hague and Helsinki.

To judge the potential impact of transport-related policies, the first step is to evaluate how the policy affects traffic flows. A variety of models have been developed for this purpose, mainly to support transport planning. Running these models requires both the necessary data and computer and appropriate expertise. These were not available, so in two of the case studies (the Hague, Helsinki) the municipal transport departments were asked to perform the traffic modelling. An important advantage of this was that a locally developed model including local data was used. Here, the two models are described and references given for further reading.

Traffic modelling in the Hague

To calculate the impact of the traffic circulation plan (VCP) on traffic flows, the city of The Hague used a multi-modal traffic model (the so-called Haaglanden model). The model covers cars, trucks, public transport and bicycles and simulates the four stages of trip generation, destination, travel mode and route choice. It is a discrete choice-model, derived from McFadden’s theory of utility maximisation by households, and based on revealed preferences. The most important parameter influencing choice of travel mode in a discrete-choice model is cost; the monetary (e.g. parking, fuel) and non-monetary (e.g. time) costs of a journey, differentiated per motive.

In this case, the study area comprised the city of The Hague and its neighbouring cities (Leiden, Zoetermeer, Rotterdam), represented by 990 zones, each attributed by a set of socio-economic data for the base year 2003 (i.e. number of inhabitants, average household size, percentage (un)employed, number of jobs in shops, number of other jobs, car ownership per 1000 inhabitants). Outputs of the model were the traffic flows on working days, between 16:00 and 18:00 hours. Trips were considered to occur for two general purposes: commuting and travel for other reasons. A database was therefore generated using data from the OVG 2003 (Onderzoek VerplaatsingsGedrag=Research on transportation behaviour), giving the number of trips by motive and area, and during evening rush hours. Destinations and travel mode choice were calculated in ten simultaneous steps. When the model was run, the initial distribution of motorised traffic over the network caused congestion; the model thus attempted to minimise this in a second distribution round - for example by using alternative modes or routes. Subsequent distribution rounds followed until, after the tenth iteration, equilibrium was reached.

The results from the model have been validated by extensive traffic measurements, carried out by the Ministry of Transport, Public Works and Water Management and the Province of South-Holland,as well as by traffic counts performed by the municipalities and by Connexxion, Prorail and HTM for public transport. The modelled effect of the VCP varies per road. On average, motorised traffic flows in the city centre would decrease by about 5 to 10%, whereas bicycle use would increase by 5%.

Results of the model were used as inputs for noise and air quality modelling for scenarios both with and without VCP implementation (carried out by DGMR, consulting engineers, and commissioned by the municipality of The Hague). The results were also used in accident modelling by the Dutch Institute for Road Safety Research (SWOV) and in the modelling of the benefits of increased physical activity due to increased bicycle use.

Traffic modelling in Helsinki

In Helsinki, traffic modelling was carried out by the Transport Department of the Helsinki Metropolitan Area Council (HMAC). HMAC uses theEMME/2 traffic forecasting system, tailored for routine long-term traffic planning in the metropolitan area (Elolähde, 2006), with predictions currently running up to year 2030. The system utilises logit models to estimate hourly traffic volumes (by vehicle type) and speeds for ~3200 unidirectional links representing the major roads and streets of the HMA. A customised model for congestion charging scenarios had been previously developed, and this was used, with some modifications, for this case study by feeding in contextual data (e.g. housing, jobs, costs) from the year 2005.

A simple description of the traffic model (from Elolähde, 2006) is given in the attached file.

References

• Elolähde, T., 2006 Traffic model system and emission calculations of the Helsinki Metropolitan Area Council. Paper presented to 20th International Emme Users' Conference, Montreal.
• Höök, B., 2007 Trängselskatt - dag 1. Pressträff Trafik Stockholm Stockholm Vägverket.
• Oum, T. H., Waters, W. G. and Yong, J. S., 1992 Concepts of price elasticities of transport demand and recent empirical estimates - an interpretative survey. Journal of Transport Economics and Policy 26, 139-154.
• Rogers, M. and Eagney, C., 2008 Congestion charging in Dublin. Proceedings of the Institution of Civil Engineers-Transport 161, 143-147.
• Roth, G. and Viloria Jr, O. 2001 Finances of commercialized urban road network subject to congestion pricing. Transportation Research Record: Journal of the Transportation Research Board 1747, 29-35.
• TFL 2007 Central London congestion charging. Impacts monitoring. Fifth Annual Report. London: Transport for London.

See also

• File:Traffic modeling in iehias.doc