ITSC 2025 Paper Abstract

Paper TH-EA-T18.3

Qiu, Shuhan (Tongji University), Huang, Xiannan (tongji university), Qin, Guoyang (Tongji University), Chen, Xuejian (Tongji University), Sun, Jian (Tongji University)

Generalized Route Choice Modeling Via Fusion of Structural Knowledge and LLM-Inferred Context

Scheduled for presentation during the Invited Session "S18b-Innovative Applications of LLM in Multimodal Transportation Systems" (TH-EA-T18), Thursday, November 20, 2025, 14:10−14:30, Southport 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords AI, Machine Learning for Real-time Traffic Flow Prediction and Management, AI, Machine Learning Techniques for Traffic Demand Forecasting

Abstract

Route choice modeling (RCM) underpins travel behavior analysis, traffic policy evaluation, and network performance forecasting. Despite advances from discrete choice models to deep learning, existing methods struggle to integrate implicit contextual factors—such as spatial semantics—beyond explicit link attributes. Here, we propose a generalized link-based RCM framework that fuses structural knowledge from classical models with Large Language Model (LLM)-inferred context to both streamline the modeling process and boost predictive performance. Our approach tokenizes link transitions, enabling the seamless integration of structured features with LLM-derived semantics to enhance expressiveness and behavioral fidelity. We identify three key mechanisms: (1) granularity alignment between links and tokens for structured-context fusion; (2) implicit information compensation through LLMs' encoded environmental information; and (3) controlled generalization anchored by domain constraints and trajectory data. Experiments on diverse OD transfer tasks show that our LLM-enhanced models reduce path flow Jensen-Shannon divergence by up to 45% compared to traditional Recursive Logit (RL), and outperform deep RL (DRL) models by 10–20% on average. These results advance a unified paradigm for integrating data, structural knowledge, and contextual information to improve traveler behavior modeling.