Route choice modeling using dynamic discrete choice models

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Traffic congestion is a serious problem in cities in Canada and worldwide. Transport models are used as a decision tool for traffic management, evaluation of new infrastructure or impacts of road construction work. This research program focuses on the central issue of predicting travelers' route choices in such models. The state-of-the-art models that are based on real data, i.e. observations of travelers' behavior in a given city, cannot accurately deal with congested networks. In fact, the important aspect that travel time is uncertain to drivers is ignored in the models. In this research program we propose models that can be based on real data and that can deal with congested networks but still being operational in practice. This research therefore contributes to improved transport models which in turn can be used to alleviate congestion.