Modelling Parking and Park & Ride
Back to Visual-tm home Back to overview Visual-tm can now model combined-mode trips so that car drivers can park their car and take public transport or walk for the rest of their trip. You specify each car park or park and ride site in the network description as a car park, with its parking capacity, parking charge and other parameters. The path builder will find the least generalised cost route taking account of whether to drive all the way, park at a car park or take any one of your park and rides. It takes account of all the public transport attributes including fares, in-vehicle time, walk time, wait time, interchanges between different modes, boarding and alighting as well as the usual car driving attributes including parking cost, in-vehicle time, distance, fuel costs, resource costs, junction delay and link travel time.
It covers both the car driver and the car passengers. The car (ie the car driver) is assigned to the highway network and is used for junction delay and capacity restraint purposes while the passengers plus the driver are assigned to the public transport (which includes the walk) network and travel on to their destination on public transport – perhaps walking for some of the way at the end of their trip or walking all the way from the car park. The path builder compares the generalised cost of all paths and selects the least generalised cost path as the reference path. In the case of multi-routing, trips are spread between the public transport paths using the multi-routing option chosen.
Park and ride could be provided by bus or by any mode of transport eg LRT or rail. It includes parking at railway stations – anywhere there is a car park and an interchange onto public transport.
The paths are sensitive to congestion because when speeds drop, in-vehicle times by car drop, bus speeds, which are a function of car speeds, will also drop (depending upon the speed relationship chosen) thereby changing the relative in-vehicle times and hence the paths.
The paths are also sensitive to the capacity of the car parks. You supply a function which relates the delay in parking to the number of vehicles trying to park and to the car park capacity. As the number of cars trying to park increases the time taken to park increases until the car park reaches capacity when vehicles start queuing until a space is available. The assignment model is iterated so that at the first iteration the car park may be well over capacity so that the capacity restraint model would calculate a high parking delay time. At the next iteration, this high parking delay time would add to the travel time and hence fewer paths would chose the park and ride and it would get less vehicles parking which at the next capacity restraint iteration would provide less delay which would lead to more vehicles parked and so on. The model is iterated until the cars parked are at or below capacity (or conceivably over capacity in which case you need to do something about providing more space or expect big queues!)
Visual-tm also covers the Kiss & Ride case where a driver takes a passenger to a railway (or LRT, Metro etc) station to drop them off and drives back home. In this case just the passenger (or passengers) take public transport. To include the driver returning home, Visual-tm outputs a matrix of trips from the railway station to the home zone, which can be assigned back to the network in the usual way. This matrix is also important for appraisal. Visual-tm covers the case where a cyclist cycles to the train (LRT, Metro, bus or any other public transport mode) leaves his bike at the station and continues on his trip by public transport.
Mode choice is best taken out of the assignment process and put into a separate mode (or sub mode) choice model. In this case Visual-tm’s path builder can be used as described above to select paths via each alternative (the method for biasing paths is already in Visual-tm). The paths can be skimmed with Visual-tm’s new enhanced skim functionality which separates the private vehicle and the public transport components of the attributes (in-vehicle time, distance, costs etc) for input into the mode choice model. Through the mode choice model, parking and park & ride can then be fed through into trip distribution and other behavioural response models (via the logsum) so that they can influence trip distribution and overall travel making.
This new functionality now enables parking, park & ride and related combined-mode trips to be incorporated properly into transport models. It represents a major step forward in transport modelling capability.
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