Shared Autonomous Vehicle (SAV) has many impacts on the transport development, such as saving parking space. However, SAV meets a huge challenge in terms of vehicle supply and user demand imbalance. The traditional mathematical optimization method cannot be well used due to the computational burden. Hence, this paper proposes a Reinforcement Learning (RL) based SAV relocation approach. First, two types of RL agents, car-based and zone-based agents, are developed as agents for vehicles and stations, respectively. Then, the RL scheme is trained by using historical demand data to facilitate real-time carsharing relocation. Finally, to compare the proposed two types of RL methods, three scenarios are used: small-scale, middle-scale, and large-scale networks. Solutions indicate that the enhanced zone-based method achieves an additional 146% profit compared to traditional threshold-based relocation strategies. The user travel behaviour impacts are provided by analyzing parking demand and travel movements among residential, industrial and commercial zones.

中文翻译：

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通过车辆运动和用户出行行为共享自动驾驶车辆运营决策


共享自动驾驶汽车（SAV）对交通发展有很多影响，例如节省停车空间。然而，SAV面临着车辆供给与用户需求不平衡的巨大挑战。传统的数学优化方法由于计算负担而不能很好地使用。因此，本文提出了一种基于强化学习（RL）的SAV重定位方法。首先，开发了两种类型的 RL 代理：基于汽车的代理和基于区域的代理，分别作为车辆和车站的代理。然后，利用历史需求数据训练强化学习方案，以促进实时汽车共享搬迁。最后，为了比较所提出的两种类型的 RL 方法，使用了三种场景：小规模、中规模和大规模网络。解决方案表明，与传统的基于阈值的搬迁策略相比，增强的基于区域的方法可额外获得 146% 的利润。通过分析住宅区、工业区和商业区之间的停车需求和出行运动，提供用户出行行为影响。