Composite traffic model

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This page is about a composite traffic model that is an updated version of File:Composite traffic.ANA. The new version is coded with R.

Definition

Variables

• Trip rate on a workday in the Helsinki metropolitan area
• Route matrix in the Helsinki metropolitan area
• Distance matrix in the Helsinki metropolitan area

R model

• Trip aggregator
  • Optimization rules:

1. No second transfer -> prioritize "secondary" passengers
2. Fill as many 8-person-vehicles as possible
3. Fill as many 4-person-vehicles as possible
4. Special rule: for trips with no possible transfer-point -> direct trip
5. Transfer the rest (will always be 4-person-vehicles)
6. Re-check vehicle configurations, when exact numbers of primary and secondary passengers as well as transferees are known

+ Show code- Hide code # Trip aggregator, sampled passenger data as input n.intervals.per.h <- 5 trips.next <- data.frame() trips.left <- data.frame() trips.out <- data.frame() trips.secondary <- data.frame() times <- seq(1, 25, 1 / n.intervals.per.h) times[length(times)] <- 1 library(OpasnetBaseUtils) roads <- op_baseGetData("opasnet_base", "Op_en2634", series_id = 2575, apply.utf8 = FALSE) colnames(roads)[6] <- "Through" distance <- op_baseGetData("opasnet_base", "Op_en5322", include = 14299, exclude = c(53097, 53098)) speed <- 40 # kmh^-1 tdelay <- distance[distance$Mode == "Car" & distance$Time == "1", c("From", "To", "Result")] tdelay$Result <- ceiling((tdelay$Result / speed) * n.intervals.per.h) / n.intervals.per.h colnames(tdelay)[colnames(tdelay) %in% "Result"] <- "Delay" trips <- op_baseGetData("opasnet_base", "Op_en2625", include = 53096) for (i in 1:(length(times) - 1)) { if(i == 1) { trips.sample.1 <- trips[trips$Time == times[1],] trips.sample.1$Secondary <- 0 } else { trips.sample.1 <- trips.sample.2 trips.sample.1 <- merge(trips.sample.1, trips.secondary, all.x = TRUE) trips.sample.1$Secondary[is.na(trips.sample.1$Secondary)] <- 0 } trips.sample.2 <- trips[trips$Time == times[i + 1],] # Optimizer main code optimal.d.trips <- 0 sub.optimal.d.trips <- 0 optimal.d.trips <- (trips.sample.1$Result + trips.sample.1$Secondary) %/% 4 * 4 sub.optimal.d.trips <- ifelse(trips.sample.1$Secondary - optimal.d.trips > 0, trips.sample.1$Result + trips.sample.1$Secondary - optimal.d.trips, 0) busiest <- tapply(trips.sample.2$Result, trips.sample.2$From, sum) busiest <- sort(busiest, decreasing = TRUE) condition <- trips.sample.1$Result + trips.sample.1$Secondary - optimal.d.trips - sub.optimal.d.trips > 0 trips.next <- merge(trips.sample.1[condition, c("From","To")], roads[,c("From","To","Through")], all.x = TRUE) trips.next$Through <- match(trips.next$Through, names(busiest)) checkpoints <- tapply(trips.next$Through, trips.next[,c("From", "To")], min) trips.sample.1 <- merge(trips.sample.1, as.data.frame(as.table(checkpoints)), all.x = TRUE) colnames(trips.sample.1)[colnames(trips.sample.1) == "Freq"] <- "Checkpoint" trips.sample.1$Checkpoint <- names(busiest)[trips.sample.1$Checkpoint] # Take into account those that don't have a checkpoint condition2 <- is.na(trips.sample.1$Checkpoint) condition3 <- trips.sample.1$Result + trips.sample.1$Secondary - optimal.d.trips - sub.optimal.d.trips > 0 no.transfer <- ifelse(condition2 & condition3, (trips.sample.1$Result + trips.sample.1$Secondary - optimal.d.trips - sub.optimal.d.trips)[condition2 & condition3], 0) trips.sample.1$Optim.d.trips <- optimal.d.trips trips.sample.1$Sub.optim.d.trips <- sub.optimal.d.trips trips.sample.1$No.transfer <- no.transfer # Transfers trips.left.trans <- data.frame(trips.sample.1[!condition2 & condition3, c("From", "Checkpoint", "To", "Time")], Transferred = (trips.sample.1$Result + trips.sample.1$Secondary - optimal.d.trips - sub.optimal.d.trips)[!condition2 & condition3]) colnames(trips.left.trans)[1] <- "From" colnames(trips.left.trans)[3] <- "Destination" colnames(trips.left.trans)[2] <- "To" trips.sample.1 <- merge(trips.sample.1, as.data.frame(as.table(tapply(trips.left.trans$Transferred, trips.left.trans[,c("From","To")], sum))), all.x = TRUE) colnames(trips.sample.1)[colnames(trips.sample.1) %in% "Freq"] <- "Transferred" trips.sample.1$Transferred[is.na(trips.sample.1$Transferred)] <- 0 # Now divide passengers to cars n.full.8.cars <- (trips.sample.1$Optim.d.trip + trips.sample.1$Sub.optim.d.trip + trips.sample.1$No.transfer + trips.sample.1$Transferred) %/% 8 n.full.4.cars <- (trips.sample.1$Optim.d.trip + trips.sample.1$Sub.optim.d.trip + trips.sample.1$No.transfer + trips.sample.1$Transferred - n.full.8.cars * 8) %/% 4 n.4.cars.3.pas <- (trips.sample.1$Optim.d.trip + trips.sample.1$Sub.optim.d.trip + trips.sample.1$No.transfer + trips.sample.1$Transferred - n.full.8.cars * 8 - n.full.4.cars * 4) %/% 3 n.4.cars.2.pas <- (trips.sample.1$Optim.d.trip + trips.sample.1$Sub.optim.d.trip + trips.sample.1$No.transfer + trips.sample.1$Transferred - n.full.8.cars * 8 - n.full.4.cars * 4 - n.4.cars.3.pas * 3) %/% 2 n.4.cars.1.pas <- trips.sample.1$Optim.d.trip + trips.sample.1$Sub.optim.d.trip + trips.sample.1$No.transfer + trips.sample.1$Transferred - n.full.8.cars * 8 - n.full.4.cars * 4 - n.4.cars.3.pas * 3 - n.4.cars.2.pas * 2 d8 <- ifelse(trips.sample.1$Optim.d.trip + trips.sample.1$Sub.optim.d.trip + trips.sample.1$No.transfer < 8 * n.full.8.cars, trips.sample.1$Optim.d.trip + trips.sample.1$Sub.optim.d.trip + trips.sample.1$No.transfer, 8 * n.full.8.cars) d4 <- ifelse(trips.sample.1$Optim.d.trip + trips.sample.1$Sub.optim.d.trip + trips.sample.1$No.transfer - d8 < 4 * n.full.4.cars, trips.sample.1$Optim.d.trip + trips.sample.1$Sub.optim.d.trip + trips.sample.1$No.transfer - d8, 4 * n.full.4.cars) c8 <- 8 * n.full.8.cars - d8 c4 <- 4 * n.full.4.cars - d4 c3 <- n.4.cars.3.pas * (trips.sample.1$Transferred - c8 - c4) # Note: there will be only 1 partially filled car d3 <- 3 * n.4.cars.3.pas - c3 c2 <- n.4.cars.2.pas * (trips.sample.1$Transferred - c8 - c4) d2 <- 2 * n.4.cars.2.pas - c2 c1 <- n.4.cars.1.pas * (trips.sample.1$Transferred - c8 - c4) d1 <- n.4.cars.1.pas - c1 # Add transferred passengers to the next time slot as secondary passengers trips.left.trans <- merge(trips.left.trans, tdelay) colnames(trips.left.trans)[5] <- "Secondary" colnames(trips.left.trans)[1] <- "Origin" colnames(trips.left.trans)[2] <- "From" colnames(trips.left.trans)[3] <- "To" trips.left.trans$Time <- as.character(as.numeric(as.character(trips.left.trans$Time)) + trips.left.trans$Delay) trips.left.trans <- as.data.frame(as.table(tapply(trips.left.trans$Secondary, trips.left.trans[, c("From","To","Time")], sum))) colnames(trips.left.trans)[4] <- "Secondary" trips.left.trans <- trips.left.trans[!is.na(trips.left.trans$Secondary),] trips.secondary <- rbind(trips.secondary, trips.left.trans) trips.out <- rbind(trips.out, data.frame(trips.sample.1[, c("From", "To", "Time")], d8, d4, d3, d2, d1, c8, c4, c3, c2, c1)) } # Summary d8 <- tapply(trips.out$d8, trips.out$Time, sum) d4 <- tapply(trips.out$d4, trips.out$Time, sum) d3 <- tapply(trips.out$d3, trips.out$Time, sum) d2 <- tapply(trips.out$d2, trips.out$Time, sum) d1 <- tapply(trips.out$d1, trips.out$Time, sum) c8 <- tapply(trips.out$c8, trips.out$Time, sum) c4 <- tapply(trips.out$c4, trips.out$Time, sum) c3 <- tapply(trips.out$c3, trips.out$Time, sum) c2 <- tapply(trips.out$c2, trips.out$Time, sum) c1 <- tapply(trips.out$c1, trips.out$Time, sum) test <- data.frame(Time = names(d8), Type = rep(colnames(trips.out)[4:13], each = length(d8)), Result = c(d8, d4, d3, d2, d1, c8, c4, c3, c2, c1)) test$Time <- as.numeric(as.character(test$Time)) library(ggplot2) ggplot(test, aes(x = Time, y = Result, fill = Type)) + geom_area() # Vehicle kilometers d8 <- tapply(trips.out$d8, trips.out[,c("From","To")], sum) d4 <- tapply(trips.out$d4, trips.out[,c("From","To")], sum) d3 <- tapply(trips.out$d3, trips.out[,c("From","To")], sum) d2 <- tapply(trips.out$d2, trips.out[,c("From","To")], sum) d1 <- tapply(trips.out$d1, trips.out[,c("From","To")], sum) c8 <- tapply(trips.out$c8, trips.out[,c("From","To")], sum) c4 <- tapply(trips.out$c4, trips.out[,c("From","To")], sum) c3 <- tapply(trips.out$c3, trips.out[,c("From","To")], sum) c2 <- tapply(trips.out$c2, trips.out[,c("From","To")], sum) c1 <- tapply(trips.out$c1, trips.out[,c("From","To")], sum) n.full.8.cars <- as.data.frame(as.table((d8 + c8) / 8)) n.full.4.cars <- as.data.frame(as.table((d4 + c4) / 4)) n.4.cars.3.pas <- as.data.frame(as.table((d3 + c3) / 3)) n.4.cars.2.pas <- as.data.frame(as.table((d2 + c2) / 2)) n.4.cars.1.pas <- as.data.frame(as.table(d1 + c1)) colnames(n.full.8.cars)[3] <- "n.full.8.cars" colnames(n.full.4.cars)[3] <- "n.full.4.cars" colnames(n.4.cars.3.pas)[3] <- "n.4.cars.3.pas" colnames(n.4.cars.2.pas)[3] <- "n.4.cars.2.pas" colnames(n.4.cars.1.pas)[3] <- "n.4.cars.1.pas" test2 <- merge(n.full.8.cars, n.full.4.cars) test2 <- merge(test2, n.4.cars.3.pas) test2 <- merge(test2, n.4.cars.2.pas) test2 <- merge(test2, n.4.cars.1.pas) library(reshape) test2 <- melt(test2, id.vars = c("From","To"), variable_name = "Type") test2 <- merge(test2, distance[,c("From", "To", "Result")]) colnames(test2)[5] <- "Distance" test2$vehicle.kilometers <- test2$value * test2$Distance # Costs test3 <- as.data.frame(as.table(tapply(test2$vehicle.kilometers, test2$Type, sum))) colnames(test3) <- c("Type", "vehicle.kilometers") fuel.cons.emis.CO2 <- data.frame(Type = c("n.full.8.cars", "n.full.4.cars", "n.4.cars.3.pas", "n.4.cars.2.pas", "n.4.cars.1.pas"), Consumption = c(8.7, 5.7, 5.7, 5.7, 5.7) / 100, Emis.factor.CO2 = c(232, 153, 153, 153, 153)) emis.factor.PM <- 0.1 # gkm^-1 fuel.price <- 1.374 # 4.8. average cost of a liter of diesel fuel in Finland maint.price <- 0.0582 # €km^-1 driver.salary <- 2313 / 160 * 1.35 # €h^-1 test3 <- merge(test3, fuel.cons.emis.CO2) test3$Fuel.cost <- test3$vehicle.kilometers * test3$Consumption * fuel.price test3$Maint.cost <- test3$vehicle.kilometers * maint.price test3$Emis.CO2 <- test3$vehicle.kilometers * test3$Emis.factor.CO2 test3$Emis.PM <- test3$vehicle.kilometers * emis.factor.PM test3$Driver.cost <- test3$vehicle.kilometers / speed * driver.salary PM.lethality <- c(-7.223e-004, 5.640e-006, 4.228e-005, 5.987e-005, 8.013e-005, 1.150e-004, 2.037e-004, 2.939e-004, 3.598e-004, 4.132e-004, 4.640e-004, 5.139e-004, 5.662e-004, 6.233e-004, 6.854e-004, 7.577e-004, 8.441e-004, 9.519e-004, 1.093e-003, 1.314e-003, 2.805e-003) PM.lethality <- median(PM.lethality) # deaths / kg value.of.life <- (2e6 + 0.98e6) / 2 # runif(n, 0.98e6, 2e6) # € / death emis.price.PM <- PM.lethality * value.of.life # 201.879 # €kg^-1 emis.price.CO2 <- 10e-3 # runif(n, 5e-3, 40e-3) # €kg^-1, the price of CO2 allowances fluctuates quite a bit, in 8.8.2011 they # were going for 10.74 € per ton of CO2 equivalent test3$CO2.cost <- test3$Emis.CO2 * emis.price.CO2 test3$PM.cost <- test3$Emis.PM * emis.price.PM test3

Recommended R functions used in code

data.frame OpasnetBaseUtils op_baseGetData slicing R objects merge tapply match ifelse is.na colnames rbind as.numeric as.character as.data.frame as.table

TODO: {{#todo:Ajattelin yllä olevaa käytäntöä käyttöönotettavaksi: koodissa käytetyistä funktioista kirjoitetaan linkit koodin alle, jotta käyttäjä löytää helpon selityksen siitä, miten kyseistä koodia Opasnet-malleissa suositellaan käytettäväksi. Teemu ehdotti tämän sijaan, että R-toolsiin rakennetaan suoraan sanalista, jotka koodista löytyessään näkyisivät linkkeinä ohjesivuille. Liekö työläs toteuttaa? --Jouni 10:55, 22 August 2011 (EEST)|Einari Happonen, Teemu Rintala, Juha Villman|Opasnet}}

----#: . Takes below 10 minutes to run. Neat! --Teemu R 14:08, 2 August 2011 (EEST) (type: truth; paradigms: science: comment)

----#: . Though much slower on the serverside R... --Teemu R 14:26, 2 August 2011 (EEST) (type: truth; paradigms: science: comment)

Plans for further development:

• Make an input form for bus transport subsidies (user can decide)
• Based on subsidies less or more bus routes will be running.

----#: . Got any ideas on how the active bus routes should be derived from the amount of subsidies? --Teemu R 10:12, 18 August 2011 (EEST) (type: truth; paradigms: science: comment)

• If bus is not available, trips will be made by private cars.
• Calculate the impacts on costs due to a) fine particles and health, b) CO2 and climate, c) fuel costs, d) driver salary costs based on kilometres driven. Parking, rush delay, and capital costs due to vehicle maintenance are ignored. (All these ignorances are biased towards cars looking good.)
• Play with the model in the course. The model runs should be quick because there is no optimising.

⇤--#: . Though there will be more data (private + public trips vs. only private) downloaded. Which might also cause memory problems, since the data is fairly large (4M rows). --Teemu R 10:12, 18 August 2011 (EEST) (type: truth; paradigms: science: attack)

• Practical implementation: Teemu does as much of the model as he can; Sami finalises the work.

----#: . By the way, what should be the name for a new page considering the model described in these comments? Since we're no longer talking about composite traffic... Something like "Helsinki metropolitan area traffic model"? --Teemu R 14:50, 18 August 2011 (EEST) (type: truth; paradigms: science: comment)

See also

• Composite traffic
• fi:Helsingin seudun liikennemalli