Where You Go is Who You Are: Behavioral Theory-Guided LLMs for Inverse Reinforcement Learning
This work addresses the challenge of enriching big trajectory data for transportation planning by providing a more accurate and behaviorally grounded method to infer sociodemographic information, though it is incremental as it builds on existing IRL and LLM techniques.
The study tackled the problem of inferring missing sociodemographic attributes from human mobility data by introducing SILIC, a framework that uses LLMs guided by behavioral theory and inverse reinforcement learning, achieving substantial performance improvements over state-of-the-art baselines in a travel survey dataset.
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.