Data-Driven Fairness-Aware Vehicle Displacement for Large-Scale Electric Taxi Fleets

Wang, Guang; Zhong, Shuxin; Wang, Shuai; Miao, Fei; Zheng, Dong; Stankovic, John A.

doi:10.1109/icde51399.2021.00108

Cited by 28 publications

(15 citation statements)

References 21 publications

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“…In recent years, EVs have become an emerging choice in the modern transportation system due to their low-carbon emission and energy efficiency. Some efforts [1,8,14,[35][36][37][38][39]44] have been made for charging station recommendation for EVs. In particular, most studies [1,8,35,38,39] focus on recommending charging station locations for EV drivers with the goal of time.…”

Section: Related Workmentioning

confidence: 99%

“…Some efforts [1,8,14,[35][36][37][38][39]44] have been made for charging station recommendation for EVs. In particular, most studies [1,8,35,38,39] focus on recommending charging station locations for EV drivers with the goal of time. For example, Guo et al [8] propose to make recommendations of charging stations to minimize the time of traveling and queuing with a game-theoretical approach.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Guo et al [8] propose to make recommendations of charging stations to minimize the time of traveling and queuing with a game-theoretical approach. Wang et al [39] devise a fairness-aware recommender system to reduce the idle time based on the fairness constraints. Cao et al [1] introduce the charging reservation information into the vehicle-to-vehicle system to facilitate the location recommendation.…”

Section: Related Workmentioning

confidence: 99%

“…In 2018, there are over 2.61 million EVs on the road in China, and this number will reach 80 million in 2030 [48]. Although the government is expanding the publicly accessible charging network to fulfill the explosively growing on-demand charging requirement, it is still difficult for EV drivers to charge their vehicles due to the fully-occupied stations and long waiting time [3,16,35,39]. Undoubtedly, such an unsatisfactory charging experience raises undesirable charging cost and inefficiency, and may even increase the EV driver's "range anxiety", which prevents the further prevalence of EVs.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the current recommendation may influence future EV charging recommendations and produce a long-term effect on the global charging network. Previously, some efforts [1,8,35,38,39,43] have been made for charging recommendations via greedy-based strategies by suggesting the most proper station for the current EV driver in each step concerning a single objective (e.g., minimizing the overall CWT). However, such an approach overlooks the long-term contradiction between the space-constrained charging capacity and the spatiotemporally unbalanced charging demands, which leads to sub-optimal recommendations from a global perspective (e.g., longer overall CWT and higher CFR).…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning

Zhang

Líu

Wang

et al. 2021

Proceedings of the Web Conference 2021

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Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability. However, in many large cities, EV drivers often fail to find the proper spots for charging, because of the limited charging infrastructures and the spatiotemporally unbalanced charging demands. Indeed, the recent emergence of deep reinforcement learning provides great potential to improve the charging experience from various aspects over a long-term horizon. In this paper, we propose a framework, named Multi-Agent Spatio-Temporal Reinforcement Learning (Master), for intelligently recommending public accessible charging stations by jointly considering various long-term spatiotemporal factors. Specifically, by regarding each charging station as an individual agent, we formulate this problem as a multi-objective multi-agent reinforcement learning task. We first develop a multi-agent actor-critic framework with the centralized attentive critic to coordinate the recommendation between geo-distributed agents. Moreover, to quantify the influence of future potential charging competition, we introduce a delayed access strategy to exploit the knowledge of future charging competition during training. After that, to effectively optimize multiple learning objectives, we extend the centralized attentive critic to multi-critics and develop a dynamic gradient re-weighting strategy to adaptively guide the optimization direction. Finally, extensive experiments on two real-world datasets demonstrate that Master achieves the best comprehensive performance compared with nine baseline approaches.

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