Abstract
Public transport (PT) is important, because the current traffic system faces well known problems like congestion, environmental impact and use of public space. To be able to assess the effects of policy measures properly, it is necessary to model the behavior of the (PT) traveler in a realistic way. An aspect that lacks realism in a lot of current models is the rigid separation between modes: within the model a traveler cannot choose to switch between modes, so multimodal trips that combine a public transport trip with the car or with the bicycle are not (or at least not explicitly) taken into account, while the use of the bicycle as an access mode is very popular in the Netherlands, and getting more popular in other countries. Easy bike rental systems enable use as an egress mode as well. The use of the car as an access mode is very popular in the US. Furthermore, multiple routing is important, because different users have different preferences (i.e. a fast route or a route without a transfer). These two aspects are addressed in this paper, to achieve more realistic transit modeling.Multiple routing is included by further developing the method of optimal strategies, where the departure time of vehicles is taken into account in order to determine whether a choice option is the shortest route for some moment in time. This results in a static route choice algorithm that is capable to assess large scale networks. By defining a search radius for different access and egress modes and by defining a sensible set of transit lines, the calculation time of the algorithm is kept limited. Logit choice models are used for stop choice and line choice, to calculate the fractions of travelers that take each route alternative.The route choice model calculates cost matrices for several mode chains. These mode chains include single mode travel options (like the car or PT combined with walking), but also multimodal travel options (that always include a PT leg). These cost matrices are incorporated in the mode choice process with a nested logit model to determine mode choice. This results in an OD matrix for all modes and mode chains. Finally, these OD matrices are assigned to the network, again using the route choice model.Applying this modeling framework to a real world case study in the Amsterdam metropolitan area shows that the computation times are reasonable, the results are plausible and conceptually sound. This enables modelers for example to assess infrastructural network developments in large scale networks, taking into account realistic behavior of travelers, namely the combination of multiple modes to reach their destination.
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