The integrated development of city clusters has given rise to an increasing demand for intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading traditional intercity bus services by implementing demand-responsive enhancements. Nevertheless, its online operations suffer the inherent complexities due to the coupling of vehicle resource allocation among cities and pooled-ride vehicle routing. To tackle these challenges, this study proposes a two-level framework designed to facilitate online fleet management. Specifically, a novel multi-agent feudal reinforcement learning model is proposed at the upper level of the framework to cooperatively assign idle vehicles to different intercity lines, while the lower level updates the routes of vehicles using an adaptive large neighborhood search heuristic. Numerical studies based on the realistic dataset of Xiamen and its surrounding cities in China show that the proposed framework effectively mitigates the supply and demand imbalances, and achieves significant improvement in both the average daily system profit and order fulfillment ratio.

翻译：城市集群的协同发展催生了日益增长的城际出行需求。通过实施需求响应式优化，城际拼车服务在升级传统城际巴士服务方面展现出巨大潜力。然而，其在线运营面临城市间车辆资源配置与拼车路径规划耦合所带来的固有复杂性。为应对这些挑战，本研究提出一个双层框架以实现在线车队管理。具体而言，该框架上层提出一种新颖的多智能体封建强化学习模型，用于协同分配闲置车辆至不同城际线路；下层则采用自适应大邻域搜索启发式算法更新车辆路径。基于中国厦门及周边城市真实数据集的数值研究表明，所提框架有效缓解了供需失衡问题，并在日均系统利润与订单履约率方面均实现了显著提升。