A tutorial on recursive models for analyzing and predicting path choice behavior
This is an incremental tutorial for researchers in transportation science and related fields, focusing on existing recursive models without introducing new methods.
This tutorial addresses the problem of modeling path choice behavior in transportation, known as the route choice problem, by providing a comprehensive overview and analysis of recursive discrete choice models to assist future research and help novice readers understand their advantages.
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.