# A tutorial on recursive models for analyzing and predicting path choice behavior

arXiv: https://arxiv.org/abs/1905.00883 · HTML: https://mlsanity.com/paper/1905.00883/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2019.

## Built on by (most cited first, 4 of 4)

- [AI-Driven Day-to-Day Route Choice](https://mlsanity.com/paper/2412.03338/) (2024)
- [A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards](https://mlsanity.com/paper/2206.10598/) (2022)
- [Arc travel time and path choice model estimation subsumed](https://mlsanity.com/paper/2210.14351/) (2022)
- [Global path preference and local response: A reward decomposition approach for network path choice analysis in the presence of locally perceived attributes](https://mlsanity.com/paper/2307.08646/) (2023)

## 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.