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Providing personalized advice is an important objective in the development of advanced traveler information systems. In this paper, a Bayesian method to incorporate learning of users' personal travel preferences in a multimodal routing system is proposed. The system learns preference parameters incrementally based on travel choices a user makes. Existing(More)
Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. Some of these models use decision rules to support its decision making instead of principles of utility maximization. Decision rules can be derived from different modelling approaches. In a previous study,(More)
Recent policy changes and methodological advances have led to new modeling approaches of increasing complexity in transportation research. Some of these approaches require new kinds of data. Moreover, the increasing complexity of these models often also implies that more detailed data are required, leading to increased demands on respondents. This paper(More)
Existing microscopic traffic models have often neglected departure time change as a possible response to congestion. In addition, they lack a formal model of how travellers base their daily travel decisions on the accumulated experience gathered from repetitively travelling through the transport network. This paper proposes an approach to account for these(More)