A tutorial on recursive models for analyzing and predicting path choice behavior
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The problem at the heart of this tutorial consists in modeling the path choice behavior of network users. This problem has extensively been studied in transportation science and econometrics, where it is known as the route choice problem. In this literature, individuals' choice of paths are typically predicted from discrete choice models. The aim of this tutorial is to present this problem from the novel and more general perspective of inverse optimization, in order to describe the modeling approaches proposed in related research areas and thereby motivate the use of so-called recursive models. The latter have the advantage of predicting path choices without generating choice sets. In this paper, we contextualize discrete choice models as a probabilistic approach to an inverse shortest path problem with noisy data, highlighting that recursive discrete choice models in particular originate from viewing the inner shortest path problem as a parametric Markov Decision Process. We also illustrate through simple numerical examples that recursive models overcome issues associated with the path-based discrete choice models commonly found in the transportation literature.
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