Routing policy choice prediction in a stochastic network: Recursive model and solution algorithm

Tien Mai, Xinlian Yu, Song Gao and Emma Frejinger

Transportation Research Part B: Methodological, 2021, vol. 151, issue C, 42-58

Abstract: We propose a Recursive Logit (STD-RL) model for routing policy choice in a stochastic time-dependent (STD) network, where a routing policy is a mapping from states to actions on which link to take next, and a state is defined by node, time and information. A routing policy encapsulates travelers’ adaptation to revealed traffic conditions when making route choices. The STD-RL model circumvents choice set generation, a procedure with known issues related to estimation and prediction. In a given state, travelers make their link choice maximizing the sum of the utility of the outgoing link and the expected maximum utility until the destination (a.k.a. value function that is a solution to a dynamic programming problem). Existing recursive route choice models and the corresponding solution approaches are based on the assumption that network attributes are deterministic. Hence, they cannot be applied to stochastic networks which are the focus of this paper.

Keywords: Routing policy choice; Stochastic time-dependent networks; Recursive logit; Decomposition algorithm (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.trb.2021.06.016

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