LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioning

--Ride-hailing platforms face significant challenges in optimizing order dispatching and driver repositioning operations in dynamic urban environments. Traditional approaches based on combinatorial optimization, rule-based heuristics, and reinforcement learning often overlook driver income fairness, interpretability, and adaptability to real-world dynamics. T o address these gaps, we propose LLM-ODDR, a novel framework leveraging Large Language Models (LLMs) for joint Order Dispatching and Driver Repositioning (ODDR) in ride-hailing services. LLM-ODDR framework comprises three key components: (1) Multi-objective-guided Order V alue Refinement, which evaluates orders by considering multiple objectives to determine their overall value; (2) Fairness-aware Order Dispatching, which balances platform revenue with driver income fairness; and (3) Spatiotemporal Demand-A ware Driver Repositioning, which optimizes idle vehicle placement based on historical patterns and projected supply. We also develop JointDR-GPT, a fine-tuned model optimized for ODDR tasks with domain knowledge. Extensive experiments on real-world datasets from Manhattan taxi operations demonstrate that our framework significantly outperforms traditional methods in terms of effectiveness, adaptability to anomalous conditions, and decision interpretability. T o our knowledge, this is the first exploration of LLMs as decision-making agents in ride-hailing ODDR tasks, establishing foundational insights for integrating advanced language models within intelligent transportation systems. Ride-hailing platforms [1], [2] have revolutionized urban transportation by efficiently connecting passengers with vehicles through digital marketplaces. These platforms face complex real-time decision-making challenges, particularly in order dispatching (matching riders to drivers) and driver repositioning (strategically relocating idle vehicles) [3]. Tengfei Lyu is with the Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China, and The Hong Kong University of Science and Technology, Hong Kong, SAR, China (e-mail: tlyu077@connect.hkust-gz.edu.cn). Siyuan Feng is with the Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China (e-mail: siyuan.feng@polyu.edu.hk). Hao Liu is with the Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China, and also with the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China (e-mail: liuh@ust.hk).