| 接驳地铁站的共享单车源汇时空特征及其影响因素 |
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| 引用本文： | 高楹,宋辞,郭思慧,裴韬.接驳地铁站的共享单车源汇时空特征及其影响因素[J].地球信息科学,2021,23(1):155-170. |
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| 作者姓名： | 高楹 宋辞 郭思慧 裴韬 |
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| 作者单位： | 1.中国矿业大学(北京)地球科学与测绘工程学院,北京 1000832.国家煤矿水害防治工程技术研究中心,北京 1000833.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 1001014.中国科学院大学,北京1000495.江苏省地理信息资源开发与利用协同创新中心,南京210023 |
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| 摘 要： | 共享单车是解决"最后一公里"出行的有效方法,然而,人们在利用其进行接驳地铁时,常出现无车可用或车辆淤积的现象.因此,探究用于接驳地铁的共享单车的源汇时空分布特征及其影响因素对实现其供需平衡有一定意义,单车运营公司可据此进行更及时、合理的调度.为了解不同区域的共享单车在接驳地铁时使用模式的差异,本文基于不同时间段的客流特...
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| 关 键 词： | 接驳 地铁站 共享单车 源汇 时空特征 影响因素 聚类分析 地理探测器 |
| 收稿时间： | 2020-07-06 |
Spatial-temporal Characteristics and Influencing Factors of Source and Sink of Dockless Sharing Bicycles Connected to Subway Stations |
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| GAO Ying,SONG Ci,GUO Sihui,PEI Tao.Spatial-temporal Characteristics and Influencing Factors of Source and Sink of Dockless Sharing Bicycles Connected to Subway Stations[J].Geo-information Science,2021,23(1):155-170. |
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| Authors: | GAO Ying SONG Ci GUO Sihui PEI Tao |
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| Institution: | (College of Geoscience and Surveying Engineering,China University of Mining&Technology(Beijing),Beijing 100083,China;National Engineering Research Center of Coal Mine Water Hazard Controlling,Beijing 100083,China;State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China) |
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| Abstract: | Dockless sharing bicycle is an effective transportation tool to solve the "last mile" traveling problem.However, when people use it to connect to the subway, there are usually no bicycles available or too much bicycles accumulated. Therefore, exploring the spatial and temporal distributions of the source and sink of the dockless sharing bicycles used to connect to the subway and analyzing their influencing factors are of certain significance to balance the bicycles’ supply and demand. Also, bicycle operating companies can make more timely and reasonable scheduling based on this. To understand the usage patterns of dockless sharing bicycles connecting to the subway in different regions, this paper used the K-Means clustering algorithm to classify the source and sink grids of the sharing bicycles used to connect to Beijing subway stations based on the passenger flow data at different times, and further used Geo-detector to explore the dominant factors of the spatial pattern.The results show that:(1) the source and sink grids of sharing bicycles were divided into five categories respectively, namely high-frequency low-outflow source, high-frequency abnormal source, medium-frequency low-outflow source, low-frequency high-outflow source, and low-frequency low-outflow source, and highfrequency low-inflow sink, medium-frequency low-inflow sink, low-frequency high-inflow sink, low-frequency differential inflow sink, and high-frequency abnormal sink, which describes the spatial and temporal characteristics of dockless sharing bicycle source and sink;(2) In different clusters, the dominant factors of the daily average flow values of bicycles were different. Bicycle clusters located in the city center were mainly affected by location attributes and traffic attributes, while in other clusters, they were significantly affected by multiple POIs as well. Besides, in different time periods, the influence mechanism of POI was often different;(3)For the rate of net inflows(outflows), the dominant factors of the source and sink grids of each cluster were approximately the same. The lack or surplus of bicycles was mainly related to the distance between the grids and the nearest subway station or the city center.(4) In terms of the overall source and sink rates, the distance between the grids and the nearest subway station, and the amount of residential POI were the most important factors, respectively. |
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| Keywords: | connection subway station dockless sharing bicycle source and sink spatial-temporal characteristics influencing factors cluster analysis Geo-detector |
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