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Modeling And Modifying Day-To-Day Travel Behaviors: Empirical Results And Methodological Advances, Yue Tang

Doctoral Dissertations

The increasing availability of individual-level longitudinal data provides the opportunity to better understand travelers'\ day-to-day learning process of their choice alternatives, which enables potentially more accurate predictions of choice patterns in a network with uncertainties. In this thesis, an instance-based learning (IBL) model for travel choice is developed within route-choice context, where on each day a traveler's decision depends on her entire choice history in the past. Learning in this model is based on the power law of forgetting and practice, which is shown to be capable of capturing various psychological effects embedded in travelers'\ day-to-day learning process, including the …