A Recursive Logit Model with Choice Aversion and Its Application to Transportation Networks

TL;DR

This paper introduces a recursive logit model incorporating choice aversion, addressing route correlation issues, generating violations of regularity, and revealing a form of Braess's paradox in transportation networks, validated with real GPS data.

Contribution

The paper presents a novel recursive logit model that captures choice aversion, overcoming correlation problems and explaining paradoxical network effects not addressed by traditional models.

Findings

Model overcomes route correlation issues.

Removal of edges can decrease path probabilities.

Adding edges can worsen user experience, revealing a new Braess's paradox.

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.