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征,对用于接驳北京市地铁站的共享单车所产生的源、汇网格进行了K-均值聚类,并进一步利用地理探测器探究了造成这种空间分异的原因。结果表明:①源、汇网格各被分为5类,分别为高频低流出、高频异常源、中频低流出、低频高流出、低频低流出和高频低流入、中频低流入、低频高流入、低频差异流入、高频异常汇等类型,反映了共享单车源汇的时空分布特征;②在不同聚类中,共享单车的日均流量对应的主导因子有所差别,位于市中心的聚类的车辆主要受距离和交通因子的影响,而在其它聚类中则会同时受到多种POI的显著影响,且在不同时段中影响机制不同;③对于净流入(出)率而言,各聚类的源、汇网格的主导因子则大致相同,车辆的缺少或过剩主要与距地铁站或市中心的距离有关。④从整体源、汇来看,住宅类POI数量与距最近地铁站的距离分别是影响日均流量和净流入(出)率的最强的因子。
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 g
作者
高楹
宋辞
郭思慧
裴韬
GAO Ying;SONG Ci;GUO Sihui;PEI Tao(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)
出处
《地球信息科学学报》
CSCD
北大核心
2021年第1期155-170,共16页
Journal of Geo-information Science
基金
国家自然科学基金项目(41525004、42071436)。
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
connection
subway station
dockless sharing bicycle
source and sink
spatial-temporal characteristics
influencing factors
cluster analysis
Geo-detector
