Modelling Perception Updating of Travel Times in the Context of Departure Time Choice Under ITS

Traffic information is increasingly regarded as a tool to achieve a more efficient use of the road network. As traffic information is often applied in the context of routine trips, the question arises how travellers integrate traffic information with the knowledge of travel conditions gained through daily experience. To describe this process, the paper proposes a model of perception updating of travel times in the context of departure time decisions. The model applies a CHAID-based classification algorithm to describe how travellers classify trips made under various conditions (departure time and presence of traffic information) into mental classes with comparable expectations in terms of travel time. Thus, it is assumed that the learning process depends on a set of conditions, one of which is the available travel time information. The model is tested through a series of numerical experiments. The results suggest that the model describes learning and adaptation behaviour in a plausible way. Through increased experience, perception of travel times is improved, and more departure time classes are distinguished. However, this does not seem to lead to shorter travel times or higher trip utilities. Also the presence of travel time information may be, depending on the history of trip outcomes, distinguished as a significant indicator of the expected travel time. We conclude that the model provides a good starting point for the further development of learning and adaptation models in the context of ITS.