ForETaxi: Data-Driven Fleet-Oriented Charging Resource Allocation in Large-Scale Electric Taxi Networks
ForETaxi: Data-Driven Fleet-Oriented Charging Resource Allocation in Large-Scale Electric Taxi Networks
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DOI：
10.1145/3570958
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发表时间：
2023-03
影响因子：
4.1
通讯作者：
Guang Wang;Yuefei Chen;Shuai Wang;Fan Zhang;Desheng Zhang
中科院分区：
文献类型：
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作者：
Guang Wang;Yuefei Chen;Shuai Wang;Fan Zhang;Desheng Zhang
由于电动出租车的充电活动频繁，充电时间长，因此充电过程是推广电动出租车和提高其运营效率的关键。然而，由于不均匀的充电需求/供应分布、驾驶员基于行为的充电行为以及车队的城市规模，真实的时间优化充电资源分配是极具挑战性的。现有的解决方案已经利用实时上下文信息进行充电推荐，但是它们没有考虑更丰富的车队信息，导致次优的基于个体的充电推荐。在本文中，我们设计了一个数据驱动的面向车队的充电推荐系统，用于电动出租车的充电资源分配，称为ForETaxi，其目的是最大限度地减少整个车队的整体充电开销，而不是单个车辆。ForETaxi不仅考虑当前的充电请求，还通过真实的时间推断其状态来考虑附近其他电动出租车在不久的将来可能的充电请求。更重要的是，我们使用来自中国深圳市的多种类型的传感器数据，包括GPS数据，以及来自13，000多辆电动出租车的出租车交易数据，结合道路网络数据和充电站数据来实现ForETaxi。数据驱动的评估结果表明，与最先进的基于个人的推荐方法相比，我们面向车队的ForETaxi在总充电时间减少16%和排队时间减少82%方面优于它们。
Charging processes are the key to promoting electric taxis and improving their operational efficiency due to frequent charging activities and long charging time. Nevertheless, optimizing charging resource allocation in real time is extremely challenging because of uneven charging demand/supply distributions, heuristic-based charging behaviors of drivers, and city-scale of the fleets. The existing solutions have utilized real-time contextual information for charging recommendation, but they do not consider the much-richer fleet information, leading to the suboptimal individual-based charging recommendation. In this paper, we design a data-driven fleet-oriented charging recommendation system for charging resource allocation called ForETaxi for electric taxis, which aims to minimize the overall charging overhead for the entire fleet, instead of individual vehicles. ForETaxi considers not only current charging requests but also possible charging requests of other nearby electric taxis in the near future by inferring their status in real time. More importantly, we implement ForETaxi with multiple types of sensor data from the Chinese Shenzhen city including GPS data, and taxi transaction data from more than 13,000 electric taxis, combined with road network data and charging station data. The data-driven evaluation results show that compared to the state-of-the-art individual-based recommendation methods, our fleet-oriented ForETaxi outperforms them by 16% in the total charging time reduction and 82% in the queuing time reduction.