Austin Knies, Jorge Lorca, Emerson Melo
arXiv 6 Oct 2020 · Econometrics · publishedTransportation Research Part B Methodological (2021)
arXiv:2010.02398 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Fudenberg, D. and Strzalecki, T (2015) Dynamic logit with choice aversion | 1.000 | 9 | 4 | 100% |
| 2 | Mai, T (2016) A method of integrating correlation structures for a generalized recursive route choice model | 1.000 | 8 | 3 | 100% |
| 3 | Fosgerau, M., Frejinger, E., and Karlstrom, A (2013) A link based network route choice model with unrestricted choice set | 0.983 | 20 | 5 | 95% |
| 4 | Mai, T., Fosgerau, M., and Frejinger, E (2015) A nested recursive logit model for route choice analysis | 0.971 | 24 | 6 | 92% |
| 5 | Baillon, J. B. and Cominetti, R (2008) Markovian traffic equilibrium | 0.928 | 4 | 3 | 100% |
| 6 | Duncan, L. C., Watling, D. P., Connors, R. D., Rasmussen, T. K., and… (2020) Path size logit route choice models: Issues with current models, a new internally consistent approach, and parameter estimation… | 0.909 | 12 | 4 | 75% |
| 7 | Ben-Akiva, M. and Bierlarie, M (1999) Discrete choice methods and their applications to short term travel decisions | 0.909 | 8 | 3 | 75% |
| 8 | Frejinger, E. and Bierlarie, M (2007) Capturing correlation with subnetworks in route choice models | 0.737 | 3 | 2 | 100% |
| 9 | Luce, D. R. and Suppes, P (1965) Preference, utility, and subjective probability | 0.737 | 3 | 2 | 100% |
| 10 | Ben-Akiva, M. and Lerman, S (1985) Discrete Choice Analysis: Theory and Application to Travel Demand | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 38 scored citations.