Incorporation of Departure Time Choice in a Mesoscopic Transportation Model for Stockholm

Detta är en avhandling från Stockholm : Trafik och Logistik

Sammanfattning: Travel demand management policies such as congestion charges encourage car-users to change among other things route, mode and departure time. Departure time may be especially affected by time-varying charges, since car-users can avoid high peak hour charges by travelling earlier or later, so called peak spreading effects. Conventional transport models do not include departure time choice as a response. For evaluation of time-varying congestion charges departure time choice is essential.In this thesis a transport model called SILVESTER is implemented for Stockholm. It includes departure time, mode and route choice. Morning trips, commuting as well as other trips, are modelled and time is discretized into fifteen-minute time periods. This way peak spreading effects can be analysed. The implementation is made around an existing route choice model called CONTRAM, for which a Stockholm network already exists. The CONTRAM network has been in use for a long time in Stockholm and an origin-destination matrix calibrated against local traffic counts and travel times guarantee local credibility. On the demand side, an earlier developed departure time and mode choice model of mixed logit type is used. It was estimated on CONTRAM travel times to be consistent with the route choice model. The behavioural response under time-varying congestion charges was estimated from a hypothetical study conducted in Stockholm.Paper I describes the implementation of SILVESTER. The paper shows model structure, how model run time was reduced and tests of convergence. As regards run time, a 75% cut down was achieved by reducing the number of origin-destination pairs while not changing travel time and distance distributions too much.In Paper II car-users underlying preferred departure times are derived using a method called reverse engineering. This method derives preferred departure times that reproduce as well as possible the observed travel pattern of the base year. Reverse engineering has previously only been used on small example road networks. Paper II shows that application of reverse engineering to a real-life road network is possible and gives reasonable results.