A tutorial on recursive models for analyzing and predicting path choice behavior
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Abstract (arXiv)
The problem at the heart of this tutorial consists in modeling the path choice behavior of network users. This problem has been extensively studied in transportation science, where it is known as the route choice problem. In this literature, individuals' choice of paths are typically predicted using discrete choice models. This article is a tutorial on a specific category of discrete choice models called recursive, and it makes three main contributions: First, for the purpose of assisting future research on route choice, we provide a comprehensive background on the problem, linking it to different fields including inverse optimization and inverse reinforcement learning. Second, we formally introduce the problem and the recursive modeling idea along with an overview of existing models, their properties and applications. Third, we extensively analyze illustrative examples from different angles so that a novice reader can gain intuition on the problem and the advantages provided by recursive models in comparison to path-based ones.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 May 2019.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1.
4 papers.
- 2024AI-Driven Day-to-Day Route Choicecited by 13
- 2022A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardscited by 10
- 2022Arc travel time and path choice model estimation subsumedcited by 0
- 2023Global path preference and local response: A reward decomposition approach for network path choice analysis in the presence of locally perceived attributescited by 0
- 2024AI-Driven Day-to-Day Route Choice4 Dec 2024
- 2023Global path preference and local response: A reward decomposition approach for network path choice analysis in the presence of locally perceived attributes14 Jul 2023
- 2022Arc travel time and path choice model estimation subsumed25 Oct 2022
- 2022A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards18 Jun 2022
Similar, not cited
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- 2025RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles
- 2021Trajectory Modeling via Random Utility Inverse Reinforcement Learning
- 2025Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning
- 2023Massively Scalable Inverse Reinforcement Learning in Google Maps
- 2026A Lecture Note on Offline RL and IRL, Part II: Foundations of Inverse Reinforcement Learning and Dynamic Discrete Choice Models
- 2025Where You Go is Who You Are: Behavioral Theory-Guided LLMs for Inverse Reinforcement Learning
- 2025Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach
- 2022Estimating Link Flows in Road Networks with Synthetic Trajectory Data Generation: Reinforcement Learning-based Approaches