Data-Driven Fairness-Aware Vehicle Displacement for Large-Scale Electric Taxi Fleets
Data-Driven Fairness-Aware Vehicle Displacement for Large-Scale Electric Taxi Fleets
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DOI：
10.1109/icde51399.2021.00108
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Guang Wang;Shuxin Zhong;Shuai Wang;Fei Miao;Zheng Dong;Desheng Zhang
中科院分区：
文献类型：
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作者：
Guang Wang;Shuxin Zhong;Shuai Wang;Fei Miao;Zheng Dong;Desheng Zhang
由于人们对城市空气质量和能源安全的日益关注，我们正在见证出租车电气化的快速发展。传统燃油出租车和电动出租车的一个关键区别在于它们的能量补充机制，即加油或充电，这体现在两个方面：（i）充电过程更长与加油过程更短；（ii）一天中随时间变化的电价与不随时间变化的汽油价格。复杂的充电问题（例如充电时间长和动态充电定价）可能会减少电动出租车的日常运营时间和利润，并且还会在某些非高峰充电定价时段导致充电站过度拥挤。受数据驱动调查结果的启发，本文设计了一种名为 FairMove 的公平感知车辆位移系统，通过考虑乘客出行需求和出租车充电需求，提高电动出租车车队的整体盈利效率和盈利公平性。我们首先将电动出租车位移问题表述为多智能体深度强化学习，然后提出一种集中式多智能体行动评论家方法来解决该问题。更重要的是，我们使用来自中国城市深圳的真实流数据来实施和评估 FairMove，其中包括来自 20,100 多辆电动出租车的 GPS 数据和交易数据，再加上 123 个充电站的数据，据我们所知，这些充电站构成了世界上最大的全电动出租车网络。大量的实验结果表明，我们的公平感知FairMove有效提高了深圳电动出租车车队的利润效率和利润公平性，分别提高了25.2%和54.7%。
We are witnessing a rapid taxi electrification process due to the ever-increasing concern about urban air quality and energy security. A key difference between conventional gas taxis and electric taxis is their energy replenishment mechanisms, i.e., refueling or charging, which is reflected in two aspects: (i) much longer charging processes vs. short refueling processes and (ii) time-varying electricity prices vs. time-invariant gasoline prices during a day. The complicated charging issues (e.g., long charging time and dynamic charging pricing) potentially reduce electric taxis’ daily operation time and profits, and also cause overcrowded charging stations during some off-peak charging pricing periods. Motivated by a set of findings obtained from a data-driven investigation, in this paper, we design a fairness-aware vehicle displacement system called FairMove to improve the overall profit efficiency and profit fairness of electric taxi fleets by considering both the passenger travel demand and taxi charging demand. We first formulate the electric taxi displacement problem as multi-agent deep reinforcement learning, and then we propose a centralized multi-agent actor-critic approach to tackle this problem. More importantly, we implement and evaluate FairMove with real-world streaming data from the Chinese city Shenzhen, including GPS data and transaction data from more than 20,100 electric taxis, coupled with the data of 123 charging stations, which constitute, to our knowledge, the largest all-electric taxi network in the world. The extensive experimental results show that our fairness-aware FairMove effectively improves the profit efficiency and profit fairness of the Shenzhen electric taxi fleet by 25.2% and 54.7%, respectively.