Integrated vehicle assignment and routing for system-optimal shared mobility planning with endogenous road congestion
Integrated vehicle assignment and routing for system-optimal shared mobility planning with endogenous road congestion
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DOI：
10.1016/j.trc.2020.102675
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发表时间：
2020-08
影响因子：
8.3
通讯作者：
Jiangtao Liu;P. Mirchandani;Xuesong Zhou
中科院分区：
文献类型：
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作者：
Jiangtao Liu;P. Mirchandani;Xuesong Zhou
近年来，随着网络服务公司的兴起，乘车共享服务不断增长，最近出现的自动驾驶汽车应用趋势将进一步增强未来旅行者的流动性。交通管理者应该解决的一个基本问题是如何捕捉内生交通模式，涉及未来交通规划和管理所面临的新的和不确定的因素。通过集中在一个理想的系统最优（SO）的情况下，其中（i）所有的车辆都是自主的，或可以集中引导和（ii）所有乘客的接送请求可以在一开始，本文旨在整合出行需求，车辆供应，和有限的基础设施。来自不同（真实的/虚拟的）站点的可用共乘和自动驾驶车辆可以被最优地分配以满足乘客的出行请求，同时考虑容量受限网络中的内源性拥塞。在本研究中采用了多种分解方法。针对这一基本问题，提出了一种基于弧的空时状态（STS）网络车辆整数线性规划模型，并采用Dantzig-Wolfe分解法求解。从动态交通分配的角度来看，一个空间-时间-状态（STS）的路径为基础的基于流的线性规划模型也提供了作为一个近似，根据车辆和乘客之间的映射信息，车辆和空间-时间弧在我们的优先级生成的列池中的每个STS路径。最后，数值实验证明了我们的分解方法和它们的计算效率。从我们的初步实验中，我们有一些有趣的观察：（i）在不考虑道路拥堵的情况下，网络性能/效率可能会被高估;（ii）乘客所需的上下车时间窗口可以作为缓解道路拥堵的缓冲，而不会影响系统性能;（iii）在集中控制下，拼车服务可以降低总的交通系统成本。
Ride-sharing services, that have been growing in recent years with the start of network service companies, will be further enhanced by the recently emerging trend of applications for autonomous vehicles for future traveler mobility. One fundamental question that transportation managers should address is how to capture the endogenous traffic patterns involving the new and uncertain elements facing future transportation planning and management. By concentrating on one ideal system optimal (SO) scenario, in which (i) all vehicles are autonomous, or can be centrally guided and (ii) all passengers’ pickup/drop-off trip requests can be given at the beginning, this paper aims to integrate travel demand, vehicle supply, and limited infrastructure. Available ride-shared and autonomous vehicles, from different (real/virtual) depots, can be optimally assigned to satisfy passengers’ trip requests, while considering the endogenous congestion in capacitated networks. A number of decomposition approaches are adopted in this research. Focusing on this primal problem, we propose an arc-based vehicle-based integer linear programming model in space-time-state (STS) networks, which is solved by Dantzig-Wolfe decomposition. From the perspective of dynamic traffic assignment, a space-time-state (STS) path-based flow-based linear programming model is also provided as an approximation according to the mapping information between vehicle and passenger, and between a vehicle and the space-time arc in each STS path in our priori-generated column pool. Finally, numerical experiments are performed to demonstrate our decomposition approaches and their computation efficiency. From our preliminary experiments, we have a few interesting observations: (i) without considering road congestion, the network performance/efficiency could be overestimated; (ii) passengers’ required pickup and drop-off time windows could be a buffer to mitigate road congestion, without impacting system performance; (iii) the ride-sharing service could reduce the total transportation system cost under centralized control.