Shared autonomous vehicle operational decisions with vehicle movement and user travel behaviour

IF 5.1 2区 工程技术 Q1 TRANSPORTATION Travel Behaviour and Society Pub Date : 2024-06-26 DOI:10.1016/j.tbs.2024.100848
Kai Huang , Chengqi Liu , Chenyang Zhang , Zhiyuan Liu , Hanfei Hu
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Abstract

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.

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根据车辆行驶和用户出行行为共享自动驾驶车辆的运行决策
共享自动驾驶汽车(SAV)对交通发展有很多影响,比如节省停车空间。然而,SAV 在车辆供应和用户需求不平衡方面面临巨大挑战。由于计算负担,传统的数学优化方法无法得到很好的应用。因此,本文提出了一种基于强化学习(RL)的 SAV 迁移方法。首先,开发了两种 RL 代理,即基于车的代理和基于区域的代理,分别作为车辆和站点的代理。然后,利用历史需求数据对 RL 方案进行训练,以促进汽车共享的实时迁移。最后,为了比较所提出的两种 RL 方法,使用了三种场景:小规模、中等规模和大规模网络。解决方案表明,与传统的基于阈值的搬迁策略相比,基于区域的增强型方法实现了 146% 的额外利润。通过分析住宅区、工业区和商业区之间的停车需求和出行动向,提供了用户出行行为的影响。
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来源期刊
CiteScore
9.80
自引率
7.70%
发文量
109
期刊介绍: Travel Behaviour and Society is an interdisciplinary journal publishing high-quality original papers which report leading edge research in theories, methodologies and applications concerning transportation issues and challenges which involve the social and spatial dimensions. In particular, it provides a discussion forum for major research in travel behaviour, transportation infrastructure, transportation and environmental issues, mobility and social sustainability, transportation geographic information systems (TGIS), transportation and quality of life, transportation data collection and analysis, etc.
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