Hung Tran, Tien Mai, Minh Ha Hoang
arXiv 1 Sep 2025 · Econometrics
arXiv:2509.01595 · PDF · DOI · OpenAlex · Extracted main text
The recursive logit (RL) model has become a widely used framework for route choice modeling, but it suffers from a key limitation: it assigns nonzero probabilities to all paths in the network, including those that are unrealistic, such as routes exceeding travel time deadlines or violating energy constraints. To address this gap, we propose a novel Constrained Recursive Logit (CRL) model that explicitly incorporates feasibility constraints into the RL framework. CRL retains the main advantages of RL-no path sampling and ease of prediction-but systematically excludes infeasible paths from the universal choice set. The model is inherently non-Markovian; to address this, we develop a tractable estimation approach based on extending the state space, which restores the Markov property and enables estimation using standard value iteration methods. We prove that our estimation method admits a unique solution under positive discrete costs and establish its equivalence to a multinomial logit model defined over restricted universal path choice sets. Empirical experiments on synthetic and real networks demonstrate that CRL improves behavioral realism and estimation stability, particularly in cyclic networks.
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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 | Fosgerau, M., Frejinger, E., and Karlström, A (2013) A link based network route choice model with unrestricted choice set | 1.000 | 7 | 4 | 100% |
| 2 | Rust, J (1987) Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher | 0.941 | 6 | 5 | 83% |
| 3 | Mai, T., Fosgerau, M., and Frejinger, E (2015) A nested recursive logit model for route choice analysis self | 0.928 | 5 | 4 | 80% |
| 4 | Mai, T. and Frejinger, E (2022) Estimation of undiscounted recursive path choice models: Convergence properties and algorithms self | 0.843 | 3 | 3 | 100% |
| 5 | Mai, T., Bastin, F., and Frejinger, E (2018) A decomposition method for estimating recursive logit based route choice models self | 0.737 | 3 | 2 | 100% |
| 6 | Aguirregabiria, V. and Mira, P (2009) Dynamic discrete choice structural models: A survey | 0.644 | 2 | 2 | 100% |
| 7 | Mai, T. and Jaillet, P (2020) A relation analysis of markov decision process frameworks self | 0.644 | 2 | 2 | 100% |
| 8 | Zimmermann, M. and Frejinger, E (2020) A tutorial on recursive models for analyzing and predicting path choice behavior | 0.644 | 2 | 2 | 100% |
| 9 | Mai, T., Yu, X., Gao, S., and Frejinger, E (2021) Route choice in a stochastic time-dependent network: the recursive model and solution algorithm self | 0.511 | 2 | 1 | 100% |
| 10 | Oyama, Y. and Hato, E (2017) A discounted recursive logit model for dynamic gridlock network analysis | 0.511 | 2 | 1 | 100% |
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