A latent-class adaptive routing choice model in stochastic time-dependent networks

Jing Ding-Mastera, Song Gao, Erik Jenelius, Mahmood Rahmani and Moshe Ben-Akiva

Transportation Research Part B: Methodological, 2019, vol. 124, issue C, 1-17

Abstract: Transportation networks are inherently uncertain due to random disruptions; meanwhile, real-time information potentially helps travelers adapt to realized traffic conditions and make better route choices under such disruptions. Modeling adaptive route choice behavior is essential in evaluating real-time traveler information systems and related policies. This research contributes to the state of the art by developing a latent-class routing policy choice model in a stochastic time-dependent network with revealed preference data. A routing policy is defined as a decision rule applied at each link that maps possible realized traffic conditions to decisions on the link to take next. It represents a traveler’s ability to look ahead in order to incorporate real-time information not yet available at the time of decision.

Date: 2019
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DOI: 10.1016/j.trb.2019.03.018

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