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

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 times <- 0 from <- 1 trips.sample <- data.frame() trips.next <- data.frame() trips.left <- data.frame() trips.out <- data.frame() trips.secondary <- data.frame() t.interval <- 1 trips.sample$Secondary <- 0 for (i in 1:length(times)) { if(i == 1) { trips.left <- trips.sample[trips.sample$Time <= times[i],] #trips.left$Secondary <- 0 # the "Secondary" row gives the number of trips which started elsewhere, i.e. they transfer here } else { trips.left <- trips.sample[trips.sample$Time <= times[i] & trips.sample$Time > times[i] - t.interval,] #trips.left <- merge(trips.left, trips.sample[trips.sample$Time <= times[i] & trips.sample$Time > times[i] - t.interval,]) } # Optimizer main code d8 <- (trips.left$Result + trips.left$Secondary) %/% 8 * 8 trips.left$Secondary <- trips.left$Secondary - d8 trips.left$Result <- trips.left$Result + trips.left$Secondary - (trips.left$Result + trips.left$Secondary + d8) %% 8 d4 <- (trips.left$Result + trips.left$Secondary) %/% 4 * 4 trips.left$Secondary <- trips.left$Secondary - d4 trips.left$Result <- trips.left$Result + trips.left$Secondary - (trips.left$Result + trips.left$Secondary + d4) %% 4 # Now, if there are still some passengers left who have already transferred, make direct trips for them and possible remaining new passengers d3 <- ifelse(trips.left$Secondary > 0, (trips.left$Result + trips.left$Secondary) %/% 3 * 3, 0) trips.left$Result <- trips.left$Result - d3 trips.left$Secondary <- trips.left$Secondary + trips.left$Result - ifelse(trips.left$Secondary > 0, (trips.left$Result + trips.left$Secondary + d3) %% 3, 0) d2 <- ifelse(trips.left$Secondary > 0, (trips.left$Result + trips.left$Secondary) %/% 2 * 2, 0) trips.left$Result <- trips.left$Result - d2 trips.left$Secondary <- trips.left$Secondary + trips.left$Result - ifelse(trips.left$Secondary > 0,(trips.left$Result + trips.left$Secondary + d3) %% 2, 0) d1 <- ifelse(trips.left$Secondary > 0, trips.left$Secondary, 0) trips.left$Secondary <- trips.left$Secondary - d1 # Then for remaining new passengers, find a transfer point and if (sum(trips.left$Result) > 0) { busiest <- tapply(trips$Result[trips$Time == times[i+1]], trips$From[trips$Time == times[i+1]], sum) busiest <- sort(busiest, decreasing = TRUE) trips.next <- merge(trips.left[trips.left$Result>0, c("From","To")], roads) #colnames(trips.next)[colnames(trips.next) == "From"] <- "Origin" #colnames(trips.next)[colnames(trips.next) == "Through"] <- "From" #colnames(trips.next)[colnames(trips.next) == "To"] <- "Destination" trips.next$Through <- match(trips.next$Through, names(busiest)) chekpoints <- tapply(trips.next$Through, trips.next[,c("From", "To")], min) colnames(trips.left)[colnames(trips.left) == "Freq"] <- "Checkpoint" trips.left <- merge(trips.left, as.data.frame(as.table(checkpoints)), all = TRUE) # delay <- distance / speed ifelse(is.na(trips.left$Checkpoint) & trips.left$Result + trips.left$Secondary > 0 trips.left <- # Now divide and send, and add secondary passengers to later time points etc. } trips.out <- rbind(trips.out, data.frame(trips.left[, c("From", "To", "Time")], d8, d4, d3, d2, d1)) }

TODO: {{#todo:Ruvetaan keräämään tälle sivulle matskua mallin uudesta versiosta.|Smxb|}}