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数据驱动的电动汽车充电站选址方法
引用本文:杨珍珍,高自友.数据驱动的电动汽车充电站选址方法[J].交通运输系统工程与信息,2018,18(5):143-150.
作者姓名:杨珍珍  高自友
作者单位:1. 北京交通大学 交通运输学院 交通系统科学与工程研究院, 北京 100044; 2. 北京掌行通信息技术有限公司, 北京 100085
基金项目:国家自然科学基金/ National Natural Science Foundation of China (71621001).
摘    要:电动汽车在缓解能源压力和环境污染中起着重要的作用,充电不方便成为制约电动汽车发展的一个重要原因.为了合理布局电动汽车充电站位置,本文提出一种数据驱动的电动汽车充电站选址方法.利用海量的移动位置数据提取潜在的交通需求位置,包括出发点和目的地;将地图划分为等间隔的网格,将潜在的交通需求位置与网格关联,统计每个网格内的交通需求量,需求量大的网格作为电动汽车充电站选址的候选位置.实证研究表明,该方法能够精准定位潜在用户需求量大的位置,为电动汽车充电站选址提供数据支持和决策依据.

关 键 词:交通工程  电动汽车充电站选址  数据驱动  出行需求  出发点  目的地  
收稿时间:2018-04-19

Location Method of Electric Vehicle Charging Station Based on Data Driven
YANG Zhen-zhen,GAO Zi-you.Location Method of Electric Vehicle Charging Station Based on Data Driven[J].Transportation Systems Engineering and Information,2018,18(5):143-150.
Authors:YANG Zhen-zhen  GAO Zi-you
Institution:1. Institute of Transportation System Science and Engineering, School of Traffic and Transportation, Bejing Jiaotong University, Beijing 100044, China; 2. Beijing Palmgo Information and Technology Co., Ltd., Beijing 100085, China
Abstract:Electric vehicle plays an important role in alleviating the pressure of energy and environmental pollution. Inconvenient charging is one of the main reasons for restricting the development of electric vehicles. To reasonably locate the electric vehicle charging stations, this paper presents a location method of electric vehicle charge station based on data driven. Potential traffic demand, including traffic origin and destination are extracted with massive location data. Map is divided into grids with the same interval. Traffic demand of each grid is calculated. Grids with large traffic demand are selected as the alternative location for electric charging station. Empirical study shows that the proposed method can accurately locate the potential user demand, which can provide data support and decision-making basis for the location of electric vehicle charging station.
Keywords:traffic engineering  location of electric vehicle charging station  data driven  travel demand  origin  destination  
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