Joint Rebalancing and Charging for Shared Electric Micromobility Vehicles with Energy-informed Demand
Joint Rebalancing and Charging for Shared Electric Micromobility Vehicles with Energy-informed Demand
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DOI：
10.1145/3583780.3614942
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Heng Tan;Yukun Yuan;Shuxin Zhong;Yu Yang
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文献类型：
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作者：
Heng Tan;Yukun Yuan;Shuxin Zhong;Yu Yang
共享电动微移动性（例如，共享电动自行车和电动滑板车）作为一种新兴的城市交通方式，近年来越来越受欢迎。然而，管理城市中成千上万的微型移动车辆，例如重新平衡和充电车辆以满足时空变化的需求，是具有挑战性的。现有的管理框架通常将需求视为请求的数量，而不考虑这些请求的能源消耗，这可能导致管理效率低下。为了解决这一限制，我们设计了RECOMMEND，这是一个用于共享电动微型汽车的再平衡和充电框架，具有能源信息需求，以提高系统收入。具体而言，我们首先从能源消费的角度重新定义需求，并基于最先进的时空预测方法预测未来的能源知情需求。然后，我们将预测的能源信息需求融合到基于强化学习的再平衡和充电框架的不同组件中。我们用2个月的实际电动微移动系统运行数据评估了RECOMMEND系统。实验结果表明，我们的方法可以很容易地集成到一般的RL框架中，并且在净收入方面至少比最先进的基线高出26.89%。
Shared electric micromobility (e.g., shared electric bikes and electric scooters), as an emerging way of urban transportation, has been increasingly popular in recent years. However, managing thousands of micromobility vehicles in a city, such as rebalancing and charging vehicles to meet spatial-temporally varied demand, is challenging. Existing management frameworks generally consider demand as the number of requests without the energy consumption of these requests, which can lead to less effective management. To address this limitation, we design RECOMMEND, a rebalancing and charging framework for shared electric micromobility vehicles with energy-informed demand to improve the system revenue. Specifically, we first re-define the demand from the perspective of energy consumption and predict the future energy-informed demand based on the state-of-the-art spatial-temporal prediction method. Then we fuse the predicted energy-informed demand into different components of a rebalancing and charging framework based on reinforcement learning. We evaluate the RECOMMEND system with 2-month real-world electric micromobility system operation data. Experimental results show that our method can be easily integrated into a general RL framework and outperform state-of-the-art baselines by at least 26.89% in terms of net revenue.