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

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 high-frequency 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.

Key words: connection, subway station, dockless sharing bicycle, source and sink, spatial-temporal characteristics, influencing factors, cluster analysis, Geo-detector