Tien Mai
arXiv 15 Aug 2026 · Econometrics
arXiv:2608.15339 · PDF · Extracted main text
Route and activity choice are connected levels of a common sequential mobility decision problem: activity choice determines what people do, where, and when, while route choice governs how they move between activities. This review develops a unified framework connecting transportation choice modeling with inverse reinforcement learning (IRL) and imitation learning (IL). Under explicit assumptions, recursive logit, logit dynamic discrete choice, and maximum-entropy IRL share a soft Bellman representation, while trajectory occupancies and network flows satisfy related conservation laws. However, utility, reward, policy, occupancy, constraints, and observation errors remain different estimands with different behavioral and counterfactual interpretations. We review constrained and inverse-constrained learning, occupancy-ratio and DICE methods, incomplete and mixed-quality demonstrations, graph and sequence learning, transfer, data fusion, multi-agent choice, and large language models. Our central message is that machine learning adds the greatest value when embedded within a behaviorally disciplined framework: exact transitions enforce feasibility, structured rewards preserve interpretable trade-offs, observation models address heterogeneous data sources, and network or equilibrium solvers produce coherent system outcomes. Such hybrid models can improve scalability and prediction without sacrificing behavioral identification or policy relevance.
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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 | Train, Kenneth E (2009) Discrete Choice Methods with Simulation | 1.000 | 10 | 6 | 100% |
| 2 | Mai, Tien and Jaillet, Patrick (2020) A Relation Analysis of Markov Decision Process Frameworks self | 1.000 | 7 | 5 | 100% |
| 3 | Zhao, Zhan and Liang, Yuebing (2023) A Deep Inverse Reinforcement Learning Approach to Route Choice Modeling with Context-Dependent Rewards | 1.000 | 7 | 5 | 100% |
| 4 | Arora, Saurabh and Doshi, Prashant (2021) A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress | 1.000 | 7 | 4 | 100% |
| 5 | Barnes, Matt and Abueg, Matthew and Lange, Oliver F. and Deeds, Matt… (2023) Massively Scalable Inverse Reinforcement Learning in Google Maps | 1.000 | 6 | 4 | 100% |
| 6 | Fosgerau, Mogens and Frejinger, Emma and Karlström, Anders (2013) A Link Based Network Route Choice Model with Unrestricted Choice Set | 1.000 | 6 | 4 | 100% |
| 7 | Levine, Sergey and Kumar, Aviral and Tucker, George and Fu, Justin (2020) Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems | 1.000 | 6 | 4 | 100% |
| 8 | Song, Yuchen and Li, Dawei and Ma, Zhenliang and Liu, Dongjie and Zh… (2024) A State-Based Inverse Reinforcement Learning Approach to Model Activity-Travel Choices Behavior with Reward Function Recovery | 1.000 | 6 | 4 | 100% |
| 9 | Mai, Tien and Bui, The Viet and Nguyen, Quoc Phong and Le, Tho V (2023) Estimation of Recursive Route Choice Models with Incomplete Trip Observations self | 1.000 | 6 | 3 | 100% |
| 10 | Ng, Andrew Y. and Harada, Daishi and Russell, Stuart J (1999) Policy Invariance under Reward Transformations: Theory and Application to Reward Shaping | 1.000 | 5 | 4 | 100% |
Showing the top 10 of 109 scored citations.