Abstract
Abstract In this paper, we examined travelers’ dynamic mode choice behavior under travel time variability. We found travelers’ inconsistent risk attitudes through a binary mode choice experiment. Although the results deviated from the traditional utility maximization theory and could not be explained by the payoff variability effect, they could be well captured in a cumulative prospect theory (CPT) framework. After considering the imperfect memory effect, we found that the prediction ability of the cumulative prospect theory learning (CPTL) model could be significantly improved. The experimental results were also compared with the CPTL model and the reinforcement learning (REL) model. This study empirically showed the potential of alternative theories to better capture travelers’ day-to-day mode choice behavior under uncertainty. A new definition of willingness to pay (WTP) in a CPT framework was provided to explicitly consider travelers’ perceived value increases in travel time.
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