With the emergence of the sharing economy and the rapid growth of mobile communications technologies, many novel sharing service models have been developed stemming from ride-hailing. Urban traffic congestion, coupled with energy conservation and emissions reduction, has prompted research on enhancing vehicle seat utilization in taxi service. To offer more effective and reliable ride-hailing, we consider ride-sharing problem with passenger transfer that allows passegers to transfer between vehicles at transfer stations. The problem requires simultaneous addressing the issues of request dispatching, transfer scheduling, and vehicle rebalancing. Studying such a ride-hailing model, we propose a novel joint decision framework combining deep reinforcement learning (DRL) with integer-linear programming (ILP) to solve the problem. We use ILP to obtain the optimal online dispatching and matching strategy in each decision stage, and DRL to learn the approximate state value of each vehicle that incorporates with some strategies to limit the state space and reduce the computational complexity. Performing numerical studies on the real-world trip dataset in Chengdu, we demonstrate that the proposed method outperforms several state-of-the-art methods, and that ride-sharing with passenger transfer is more beneficial than traditional ride-sharing.

随着共享经济的兴起和移动通信技术的快速发展，以网约车为基础的许多新型共享服务模式应运而生。城市交通拥堵，加上节能减排，促使人们研究提高出租车服务中的车辆座位利用率。为了提供更有效和可靠的乘车服务，我们考虑了乘客换乘的共乘问题，允许乘客在换乘站的车辆之间换乘。该问题需要同时解决请求调度、传输调度和车辆重新平衡的问题。研究这种叫车模型，我们提出了一种结合深度强化学习 (DRL) 和整数线性规划 (ILP) 的新型联合决策框架来解决该问题。我们使用ILP在每个决策阶段获得最优的在线调度和匹配策略，DRL学习每辆车的近似状态值，并结合一些策略来限制状态空间并降低计算复杂度。通过对成都真实世界的旅行数据集进行数值研究，我们证明了所提出的方法优于几种最先进的方法，并且与乘客中转的拼车比传统的拼车更有利。