A theoretical framework for modeling activity-travel scheduling decisions in non-stationary environments under conditions of uncertainty and learning

T.A. Arentze, H.J.P. Timmermans

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

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Abstract

This paper describes a Bayesian framework for modelling dynamic activity-travel choice under conditions of uncertainty and learning. The approach proposed differs in several respects from earlier approaches. First, the decisions considered are framed as re-scheduling decisions and, hence, the model takes the impact of a full actvity. schedule into account. Second, the model is based on the assumption that individuals' perceptions of uncertainty are represented as probability distributions across possible states of the (transport) system and that belief updating and structural learning can be modeled using Bayesian methods. The paper outlines the framework and discusses ways to specify and test the models in future research.
Original languageEnglish
Title of host publicationEIRASS Workshop on Progress in Activity-Based Analysis (28-31 May 2004, Maastricht, The Netherlands)
Pages1-13
Publication statusPublished - 2004

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