Hung Tran, Tien Mai, Minh Hoang Ha
arXiv 19 Oct 2025 · Econometrics
arXiv:2510.16886 · PDF · DOI · OpenAlex · Extracted main text
The recursive logit (RL) model provides a flexible framework for modeling sequential decision-making in transportation and choice networks, with important applications in route choice analysis, multiple discrete choice problems, and activity-based travel demand modeling. Despite its versatility, estimation of the RL model typically relies on nested fixed-point (NFXP) algorithms that are computationally expensive and prone to numerical instability. We propose a new approach that reformulates the maximum likelihood estimation problem as an optimization problem with equilibrium constraints, where both the structural parameters and the value functions are treated as decision variables. We further show that this formulation can be equivalently transformed into a conic optimization problem with exponential cones, enabling efficient solution using modern conic solvers such as MOSEK. Experiments on synthetic and real-world datasets demonstrate that our convex reformulation achieves accuracy comparable to traditional methods while offering significant improvements in computational stability and efficiency, thereby providing a practical and scalable alternative for recursive logit model estimation.
appendix boundary found by appendix_command · 88% of the source is main text. Read the extracted text to check this.
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 | Tran, H. and Mai, T (2024) Network-based representations and dynamic discrete choice models for multiple discrete choice analysis self | 1.000 | 9 | 3 | 100% |
| 2 | Mai, T. and Frejinger, E (2022) Undiscounted recursive path choice models: Convergence properties and algorithms self | 1.000 | 7 | 4 | 100% |
| 3 | Fosgerau, M., Frejinger, E., and Karlström, A (2013) A link based network route choice model with unrestricted choice set | 1.000 | 6 | 3 | 100% |
| 4 | Rust, J (1987) Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher | 1.000 | 5 | 4 | 100% |
| 5 | MOSEK ApS (2023) MOSEK Optimizer API for Python, Version 10.1, 2023 | 0.928 | 4 | 4 | 100% |
| 6 | Mai, T., Fosgerau, M., and Frejinger, E (2015) A nested recursive logit model for route choice analysis self | 0.894 | 7 | 4 | 71% |
| 7 | Fosgerau, M., McFadden, D., and Bierlaire, M (2013) Choice probability generating functions | 0.874 | 6 | 2 | 100% |
| 8 | Mai, T., Bastin, F., and Frejinger, E (2018) A decomposition method for estimating recursive logit based route choice models self | 0.874 | 5 | 2 | 100% |
| 9 | Mai, T (2016) A method of integrating correlation structures for a generalized recursive route choice model self | 0.737 | 3 | 3 | 67% |
| 10 | Iskhakov, F., Lee, J. H., Rust, J., Schjerning, B., and Seo, K (2016) Comment on “constrained optimization approaches to estimation of structural models” | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 41 scored citations.