Multimodal Transportation Systems

We present a case study of a multimodal routing system that takes into account both dynamic and stochastic travel time information. A multimodal network model is presented that makes it possible to model the travel time information of each transportation mode differently. This travel time information can either be static or dynamic, or either deterministic or stochastic. Next to this, a Dijkstra-based routing algorithm is presented that deals with this variety of travel time information in a uniform way. This research focuses on a practical implementation of the system, which means that a number of assumptions were made, like, for example, the modeling of the stochastic distributions, comparing these distributions, etc. A tradeoff had to be made between the performance of the system and the accuracy of the results. Experiments have shown that our system produces realistic routes in a short amount of time. It is demonstrated that routing dynamically indeed results in a travel time gain in comparison to routing statically. By making use of the additional stochastic travel time information even better (i.e. faster) and more reliable routes can be calculated. Moreover, it is shown that routing in the