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The allocation problem of electric car-sharing system: A data-driven approach

机译：电动汽车共享系统的分配问题：一种数据驱动的方法

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摘要

Car-sharing is an emerging transportation mode with increasing applications of electric vehicles (EVs). One of the important issues for one-way electric car-sharing systems (ECS) is unbalanced vehicle distributions and high relocation costs. To improve its efficiency and overall profit, this research proposes a data-driven optimization model with the consideration of demand uncertainty. Firstly, a large amount of historical order data from an ECS company are analyzed to characterize the dynamics of the vehicles and the behavioral features of the users. An important observation is that the daily demand by users, i.e., pick-ups, follows Poisson distribution; and the arrival rates vary across time exhibiting four major temporal stages. Based on this observation, this research constructs the ECS reallocation problem as a data-driven optimization model which is a combination of a probability expectation model and a linear programming problem with real-time data as input. More importantly, different from existing research, this research formulates the profit as the mathematical expectation of a discrete random variable with uncertain consumer demands. This allows for a comprehensive consideration of all possible future demands. Furthermore, driving range constraint has been considered in the proposed model as EV is the focus of this paper. A linear solution method is proposed to obtain the global optimal. At the end, the model is validated using real data from 30 ECS stations. The results indicate the daily improvement of profit could be as high as 19.05% with an average of 10.16%.
机译：共享汽车是一种新兴的交通方式，随着电动汽车（EV）的应用不断增加。单向电动汽车共享系统（ECS）的重要问题之一是车辆分配不平衡和高昂的搬迁成本。为了提高效率和总体利润，本研究提出了一种考虑需求不确定性的数据驱动的优化模型。首先，对来自ECS公司的大量历史订单数据进行了分析，以表征车辆的动力学特性和用户的行为特征。一个重要的观察结果是，用户的每日需求（即回车）遵循泊松分布;并且到达率随时间变化，表现出四个主要的时间阶段。基于此观察，本研究将ECS重新分配问题构造为数据驱动的优化模型，该模型是概率期望模型和以实时数据为输入的线性规划问题的组合。更重要的是，与现有研究不同，该研究将利润公式化为具有不确定消费者需求的离散随机变量的数学期望。这样可以全面考虑将来所有可能的需求。此外，由于EV是本文的重点，因此在建议的模型中已考虑了行驶里程约束。提出了一种线性求解方法来获得全局最优值。最后，使用来自30个ECS站的真实数据对模型进行验证。结果表明，利润的每日改善率可高达19.05％，平均为10.16％。

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  • 来源
    《Transportation Research》 |2020年第1期|102192.1-102192.21|共21页
  • 作者

  • 作者单位

    Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China;

    Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China|Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China;

    Monash Univ Dept Civil Engn Inst Transport Studies Clayton Vic Australia;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Car-sharing; Electric vehicles; Data-driven; Uncertain demand; Allocation; Optimization;

    机译：汽车共享;电动汽车;数据驱动;需求不确定;分配;优化;

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