An integrated car-and-ride sharing system for mobilizing heterogeneous travelers with application in underserved communities
An integrated car-and-ride sharing system for mobilizing heterogeneous travelers with application in underserved communities
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DOI：
10.1080/24725854.2019.1628377
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发表时间：
2020-02
影响因子：
2.6
通讯作者：
Miao-Shan Yu;Siqian Shen
中科院分区：
文献类型：
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作者：
Miao-Shan Yu;Siqian Shen
摘要快速增长的汽车共享和乘车业务在现代社会中产生了经济效益和社会影响，但两者在满足不同用户方面都有局限性，例如，低收入、服务不足的社区的旅行者。在本文中，我们考虑两种类型的用户：租用共享汽车的第1类驾驶员和需要共享乘车的第2类乘客。我们提出了一个集成的汽车和乘车共享（CRS）系统，以实现基于社区的共享交通。为了计算解决方案，我们提出了一个两阶段的方法，在第一阶段，我们确定初始汽车分配和类型1司机接受;在第二阶段，我们解决了一个随机的混合整数规划，以匹配接受类型1司机与类型2用户，并优化他们的皮卡路线下的随机旅行时间。目标是最小化总的旅行成本加上用户等待和系统超时的期望惩罚成本。我们证明了CRS系统在沃什特瑙县，密歇根州的性能测试的基础上产生的人口普查数据和不同的需求模式的实例。我们还证明了我们的分解算法基准与传统的Benders分解解决随机模型在第二阶段的计算效率。我们的研究结果表明，高需求满足率和有效的匹配和调度，低风险的等待和加班。
Abstract The fast-growing carsharing and ride-hailing businesses are generating economic benefits and societal impacts in modern society, while both have limitations to satisfy diverse users, e.g., travelers in low-income, underserved communities. In this article, we consider two types of users: Type 1 drivers who rent shared cars and Type 2 passengers who need shared rides. We propose an integrated car-and-ride sharing (CRS) system to enable community-based shared transportation. To compute solutions, we propose a two-phase approach where in Phase I we determine initial car allocation and Type 1 drivers to accept; in Phase II we solve a stochastic mixed-integer program to match the accepted Type 1 drivers with Type 2 users, and optimize their pick-up routes under a random travel time. The goal is to minimize the total travel cost plus expected penalty cost of users’ waiting and system overtime. We demonstrate the performance of a CRS system in Washtenaw County, Michigan by testing instances generated based on census data and different demand patterns. We also demonstrate the computational efficacy of our decomposition algorithm benchmarked with the traditional Benders decomposition for solving the stochastic model in Phase II. Our results show high demand fulfillment rates and effective matching and scheduling with low risk of waiting and overtime.