To assess the network effects of road pricing measures, a dynamic modeling framework was developed, which determines a route choice, departure timechoice and elastic demand equilibrium. A macroscopic dynamic multi-user class model called INDY forms the center of this modeling framework. INDY consists of three parts: route generation, route choice and dynamic networkloading. In order to correctly model the route choice behavior, the quality of the route generation process is of vital importance. If important routes are not generated in this process, than they will never be used in the assignment. Thus the number of routes needs to be sufficiently large. However, larger route sets increase the computation times drastically which is an undesired side effect. This paper explores the different methods that are available for route generation, e.g. Monte Carlo approach and staticequilibrium assignment and compares the resulting route sets to actual route choice behavior measured from a GPS survey. Based on the analysis a two-step Monte Carlo approach is recommended which excludes a large number of irrelevant routes while it does take into account principal routes between origin and destination as well as smaller route changes around the origin and destination. The project for which the calibration of the network was needed was the SpitsMijden project, which is a negative pricing program in order to give an incentive for people to avoid the morning peak. In the pilot program, GPS data was obtained for those participants whose reward would be a route planner with online traffic data. The routes chosen by these 109 participants are being reconstructed so that the overlap with the route sets can be determined. For the covering abstract see ITRD E137145.
Abstract