Autonomous vehicle technology is poised to revolutionize shared vehicle systems, offering the potential for increased efficiency and convenience. To better devise management strategies for shared autonomous vehicles, this paper addresses a real-time dispatch problem with hybrid requests, where on-demand (immediate) and pre-booked (reserved) trip requests coexist. The coexistence of these two types of request behaviors introduces considerable complexity to real-time dispatch due to the uncertainty in trip demand. We design an approximate dynamic programming (ADP) approach for making vehicle–trip assignments and vehicle relocation decisions. We first formulate the real-time vehicle dispatch problem as a dynamic program and decompose it into time-staged subproblems. To effectively handle the high-dimensional state space, we replace the value functions with tractable approximations and propose a piecewise-linear functional approximation method that captures the spatiotemporal value of vehicles. To calibrate the parameters in the approximations, we propose DualT and DualNext algorithms to provide precise dual information, thereby enhancing the accuracy of our approach. Furthermore, we propose a lookahead strategy that incorporates pre-booked request information into the ADP approach for improving real-time decision-making. We validate the effectiveness of the ADP approach through numerical experiments conducted using taxi data from Brooklyn, New York. The ADP approach outperforms benchmark policies in solution quality while maintaining computational efficiency, and the incorporation of the lookahead strategy significantly enhances the performance of the ADP approach, yielding substantial improvements. Numerical results demonstrate that integrating pre-booked requests into vehicle dispatch management can greatly enhance the system efficiency.

中文翻译：

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按需和预订请求的共享自动驾驶车辆的实时调度管理

自动驾驶汽车技术有望彻底改变共享汽车系统，提供提高效率和便利性的潜力。为了更好地设计共享自动驾驶车辆的管理策略，本文解决了混合请求的实时调度问题，其中按需（立即）和预先预订（保留）行程请求共存。由于出行需求的不确定性，这两类请求行为的共存给实时调度带来了相当大的复杂性。我们设计了一种近似动态规划（ADP）方法来制定车辆行程分配和车辆重新定位决策。我们首先将实时车辆调度问题表述为动态程序，并将其分解为分阶段的子问题。为了有效地处理高维状态空间，我们用易于处理的近似代替值函数，并提出了一种分段线性函数近似方法来捕获车辆的时空值。为了校准近似值中的参数，我们提出了 DualT 和 DualNext 算法来提供精确的对偶信息，从而提高我们方法的准确性。此外，我们提出了一种前瞻策略，将预先预订的请求信息合并到 ADP 方法中，以改进实时决策。我们通过使用纽约布鲁克林出租车数据进行的数值实验验证了 ADP 方法的有效性。ADP 方法在解决方案质量方面优于基准策略，同时保持计算效率，并且前瞻策略的结合显着增强了 ADP 方法的性能，产生了显着的改进。数值结果表明，将预订请求集成到车辆调度管理中可以大大提高系统效率。