A Bayesian latent class reinforcement learning framework to capture adaptive, feedback-driven travel behaviour

TL;DR

This paper introduces a Bayesian latent class reinforcement learning model to analyze how individuals adapt their travel preferences over time, capturing heterogeneity and learning behaviors in a driving simulator dataset.

Contribution

It develops a novel LCRL model that identifies distinct classes of travel behavior and preference evolution, estimated via Variational Bayes.

Findings

Identified three distinct behavioral classes with different adaptation strategies.

Demonstrated the model's ability to capture heterogeneity in preference formation.

Provided insights into context-dependent and persistent exploration behaviors.

Abstract

Many travel decisions involve a degree of experience formation, where individuals learn their preferences over time. At the same time, there is extensive scope for heterogeneity across individual travellers, both in their underlying preferences and in how these evolve. The present paper puts forward a Latent Class Reinforcement Learning (LCRL) model that allows analysts to capture both of these phenomena. We apply the model to a driving simulator dataset and estimate the parameters through Variational Bayes. We identify three distinct classes of individuals that differ markedly in how they adapt their preferences: the first displays context-dependent preferences with context-specific exploitative tendencies; the second follows a persistent exploitative strategy regardless of context; and the third engages in an exploratory strategy combined with context-specific preferences.