Yu, Hao (TongJi University), Zhang, ChongHao (Tongji University), Luo, Xiao (Tongji University), Yin, Wenyu (Tongji University), Liu, Zhe (Tongji University), Huang, Jinyi (Tongji university), Liu, Xuanyu (Tongji University)

Taxi Repositioning Via LLM-Driven Multi-Agent Coordination and Experience Accumulation

Scheduled for presentation during the Invited Session "S22a-Emerging Trends in AV Research" (TH-LM-T22), Thursday, November 20, 2025, 10:30−10:50, Coolangata 1

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

Abstract

Efficient taxi repositioning is essential to address the spatiotemporal mismatch between urban travel demand and vehicle supply. Traditional approaches often rely on heuristic rules or reinforcement learning, which suffer from limited generalization and poor interpretability. In this paper, we propose an LLM-driven framework for intelligent taxi repositioning that integrates three key components: (1) LLM-based decision reasoning, (2) spatiotemporal clustering for demand pattern abstraction, and (3) experience-driven responsibility assignment for adaptive policy optimization. A city-scale simulator is developed to evaluate the effectiveness of our approach. Experimental results show that our method achieves the highest response rate of textbf{70.59%}, the lowest average pickup time of textbf{153.3 seconds}, and the highest vehicle utilization rate of textbf{48.94%}, outperforming four baselines including clustering and optimization-based methods. Our simulator and code are publicly available at:https://github.com/clover-Saber/TrafficSimulator.