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.

中文翻译：

---

有关分析和预测路径选择行为的递归模型的教程

本教程的核心问题在于对网络用户的路径选择行为进行建模。这个问题已经在运输科学中进行了广泛的研究，被称为路线选择问题。在该文献中，通常使用离散选择模型来预测个人的路径选择。本文是有关离散选择模型的特定类别（称为递归）的教程，它提供了三个主要贡献：首先，为了协助将来对路线选择的研究，我们提供了有关该问题的综合背景，并将其链接到不同领域包括逆向优化和逆向强化学习。第二，我们正式介绍问题和递归建模思想，并概述现有模型，它们的性质和应用。第三，