Where You Go is Who You Are: Behavioral Theory-Guided LLMs for Inverse Reinforcement Learning

TL;DR

This paper presents SILIC, a novel framework that uses large language models guided by behavioral theory to infer sociodemographic attributes from mobility data, improving accuracy and interpretability.

Contribution

It introduces a theory-guided LLM approach for inverse reinforcement learning that models latent behavioral intentions based on the Theory of Planned Behavior.

Findings

Outperforms state-of-the-art baselines in sociodemographic inference

Effectively captures latent behavioral intentions from mobility patterns

Enhances transportation planning with behaviorally grounded data

Abstract

Big trajectory data hold great promise for human mobility analysis, but their utility is often constrained by the absence of critical traveler attributes, particularly sociodemographic information. While prior studies have explored predicting such attributes from mobility patterns, they often overlooked underlying cognitive mechanisms and exhibited low predictive accuracy. This study introduces SILIC, short for Sociodemographic Inference with LLM-guided Inverse Reinforcement Learning (IRL) and Cognitive Chain Reasoning (CCR), a theoretically grounded framework that leverages LLMs to infer sociodemographic attributes from observed mobility patterns by capturing latent behavioral intentions and reasoning through psychological constructs. Particularly, our approach explicitly follows the Theory of Planned Behavior (TPB), a foundational behavioral framework in transportation research, to model individuals' latent cognitive processes underlying travel decision-making. The LLMs further provide heuristic guidance to improve IRL reward function initialization and update by addressing its ill-posedness and optimization challenges arising from the vast and unstructured reward space. Evaluated in the 2017 Puget Sound Regional Council Household Travel Survey, our method substantially outperforms state-of-the-art baselines and shows great promise for enriching big trajectory data to support more behaviorally grounded applications in transportation planning and beyond.