Record: Joint Real-Time Repositioning and Charging for Electric Carsharing with Dynamic Deadlines
Record: Joint Real-Time Repositioning and Charging for Electric Carsharing with Dynamic Deadlines
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DOI：
10.1145/3447548.3467112
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发表时间：
2021-08
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影响因子：
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通讯作者：
Guang Wang;Zhou Qin;Shuai Wang;Huijun Sun;Zheng Dong;Desheng Zhang
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作者：
Guang Wang;Zhou Qin;Shuai Wang;Huijun Sun;Zheng Dong;Desheng Zhang
电动汽车共享，即电动汽车共享，作为一种新兴的按需出行服务，近年来在全球范围内蓬勃发展。尽管提供了方便、低成本和环保的出行方式，但由于现有的低效车队管理策略，即使用预定义的定期时间表重新部署车辆，而不能自适应高度动态的用户需求，以及没有充分考虑时变充电定价等许多实际因素，电动汽车共享服务也存在一些潜在的障碍。针对这些问题，本文设计了基于动态期限的电动共享汽车联合重新定位与充电的高效车队管理系统Record，以提高电动共享汽车的运营利润，同时满足用户实时取车和还车的需求。Record不仅考虑车辆重新定位的高度动态用户需求（即重新定位到哪里），而且考虑充电计划的时变充电定价（即在哪里充电）。为了有效地完成这两项任务，在Record中，我们设计了一种基于动态截止日期的分布式深度强化学习算法，该算法通过使用预测结合误差补偿机制生成动态截止日期，自适应搜索和学习最优位置，以实时满足高度动态和不平衡的用户需求。我们使用10个月的真实电动汽车共享数据对Record系统进行了实施和评估，大量的实验结果表明，我们的Record系统有效降低了25.8%的充电成本，减少了30.2%的工人车辆移动，同时满足了用户需求，实现了较小的运行开销。
Electric carsharing, i.e., electric vehicle sharing, as an emerging mobility-on-demand service, has been proliferating worldwide recently. Though providing convenient, low-cost, and environmentally-friendly mobility, there are also some potential roadblocks in electric carsharing services due to existing inefficient fleet management strategies, which relocate the vehicles using predefined periodic schedules without self-adapting to the highly dynamic user demand, and many practical factors like time-variant charging pricing also have not been fully considered. To remedy these problems, in this paper, we design Record, an effective fleet management system with joint Repositioning and Charging for electric carsharing based on dynamic deadlines to improve its operating profits and also satisfy users' real-time pickup and return demand. Record considers not only the highly dynamic user demand for vehicle repositioning (i.e., where to relocate) but also the time-varying charging pricing for charging scheduling (i.e., where to charge). To perform the two tasks efficiently, in Record, we design a dynamic deadline-based distributed deep reinforcement learning algorithm, which generates dynamic deadlines via usage prediction combined with an error compensation mechanism to adaptively search and learn the optimal locations for satisfying highly dynamic and unbalanced user demand in real time. We implement and evaluate the Record system with 10-month real-world electric carsharing data, and the extensive experimental results show that our Record effectively reduces 25.8% of charging costs and reduces 30.2% of vehicle movements by workers, and it also satisfies user demand and achieves a small runtime overhead at the same time.