A recursive logit model with choice aversion and its application to transportation networks

Austin Knies, Jorge Lorca and Emerson Melo

Transportation Research Part B: Methodological, 2022, vol. 155, issue C, 47-71

Abstract: We propose a recursive logit model which captures the notion of choice aversion by imposing a penalty term that accounts for the dimension of the choice set at each node of the transportation network. We make three contributions. First, we show that our model overcomes the correlation problem between routes, a common pitfall of traditional logit models, and that the choice aversion model can be seen as an alternative to these models. Second, we show how our model can generate violations of regularity in the path choice probabilities. In particular, we show that removing edges in the network may decrease the probability for existing paths. Finally, we show that under the presence of choice aversion, adding edges to the network can make users worse off. In other words, a type of Braess’s paradox can emerge outside of congestion and can be characterized in terms of a parameter that measures users’ degree of choice aversion. We validate these contributions by estimating this parameter over GPS traffic data captured on a real-world transportation network.

Keywords: Choice aversion; Recursive logit; IIA; Directed networks; Transportation networks (search for similar items in EconPapers)
JEL-codes: C00 C51 C61 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.trb.2021.10.011

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