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Exploring the spatial variations of transfer distances between dockless bike-sharing systems and metros
Journal of Transport Geography ( IF 5.899 ) Pub Date : 2021-03-31 , DOI: 10.1016/j.jtrangeo.2021.103032
Wenxiang Li , Shawen Chen , Jieshuang Dong , Jingxian Wu

Dockless bike-sharing is emerging as a convenient transfer mode for metros. The riding distances of bike-sharing to or from metro stations are defined as transfer distances between dockless bike-sharing systems and metros, which determine the service coverages of metro stations. However, the transfer distances have rarely been studied and they may vary from station to station. Therefore, this study aims to explore the influencing factors and spatial variations of transfer distances between dockless bike-sharing systems and metros. First, a catchment method was proposed to identify bike-sharing transfer trips. Then, the Mobike trip data, metro smartcard data, and built environment data in Shanghai were utilized to calculate the transfer distances and travel-related and built environment variables. Next, a multicollinearity test, stepwise regression, and spatial autocorrelation test were conducted to select the best explanatory variables. Finally, a geographically weighted regression model was adopted to examine the spatially varying relationships between the 85th percentile transfer distances and selected explanatory variables at different metro stations. The results show that the transfer distances are correlated with the daily metro ridership, daily bike-sharing ridership, population density, parking lot density, footway density, percentage of tourism attraction, distance from CBD, and bus stop density around metro stations. Besides, the effects of the explanatory variables on transfer distances vary across space. Generally, most variables have greater effects on transfer distances in the city suburbs. This study can help governments and operators expand the service coverage of metro stations and facilitate the integration of dockless bike-sharing and metros.



中文翻译:

探索无码头自行车共享系统和地铁之间的传输距离的空间变化

无基座自行车共享正在成为地铁的一种便捷交通方式。往返于地铁站的共享单车的骑行距离定义为无坞站共享系统与地铁之间的转移距离,它决定了地铁站的服务范围。但是,很少研究传输距离,并且每个站点的传输距离可能会有所不同。因此,本研究旨在探讨无座自行车共享系统与地铁之间的传输距离的影响因素和空间变化。首先,提出了一种集水区方法来识别共享单车的行程。然后,利用上海的Mobike行程数据,地铁智能卡数据和建筑环境数据来计算交通距离以及与旅行相关的建筑环境变量。接下来,进行多重共线性测试,逐步回归,进行空间自相关检验,以选择最佳的解释变量。最后,采用了地理加权回归模型来检验第85个百分位转移距离与不同地铁站的选定解释变量之间的空间变化关系。结果表明,转乘距离与每天的地铁乘车量,每天的共享自行车的乘车量,人口密度,停车场密度,人行道密度,旅游胜地的百分比,距中央商务区的距离以及地铁站周围的公交车站密度有关。此外,解释变量对转移距离的影响随空间而变化。通常,大多数变量对城市郊区的公交距离影响更大。

更新日期:2021-03-31
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