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

## TL;DR

This tutorial provides a comprehensive overview of recursive discrete choice models for analyzing and predicting network path choice behavior, highlighting their advantages over traditional path-based models and linking them to broader fields.

## Contribution

It introduces the recursive modeling approach for route choice, connects it to inverse optimization and reinforcement learning, and offers detailed examples to aid understanding.

## Key findings

- Recursive models offer advantages over path-based models.
- The tutorial links recursive models to inverse optimization and reinforcement learning.
- Illustrative examples demonstrate the benefits of recursive approaches.

## Abstract

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.