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. To 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 Value 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-Aware 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. To 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. While the current framework incurs higher computational costs than traditional methods, we show that parallel decomposition and model distillation can reduce latency to production-viable levels for deployment.

翻译：网约车平台在动态城市环境中优化订单分配与司机重定位操作面临重大挑战。基于组合优化、规则启发式及强化学习的传统方法，常忽视司机收入公平性、决策可解释性及对现实动态环境的适应性。为弥补这些不足，我们提出LLM-ODDR——一种利用大语言模型（LLM）实现网约车服务中订单分配与司机重定位（ODDR）联合决策的新型框架。该框架包含三大核心组件：（1）多目标导向的订单价值精炼模块，通过多维度目标评估确定订单综合价值；（2）公平性感知的订单分配模块，实现平台收益与司机收入公平性的平衡；（3）时空需求感知的司机重定位模块，基于历史模式与预测供需优化闲置车辆部署。此外，我们研发了经领域知识优化的微调模型JointDR-GPT，专用于ODDR任务。基于曼哈顿出租车运营真实数据集的广泛实验表明，本框架在决策有效性、异常条件适应性及可解释性方面显著优于传统方法。据我们所知，这是首次探索将大语言模型作为网约车ODDR任务决策主体的研究，为智能交通系统中整合先进语言模型奠定了方法论基础。尽管当前框架的计算成本高于传统方法，但通过并行分解与模型蒸馏技术，可将其延迟降低至生产环境可部署水平。