Spatiotemporal Characteristics of Bike-Sharing Usage around Rail Transit Stations: Evidence from Beijing, China

Abstract

As an emerging mode of transport, bike-sharing is being quickly accepted by Chinese residents due to its convenience and environmental friendliness. As hotspots for bike-sharing, railway-station service areas attract thousands of bikes during peak hours, which can block roads and pedestrian walkways. Of the many works devoted to the connection between bikes and rail, few have addressed the spatial‒temporal pattern of bike-sharing accumulating around station service areas. In this work, we investigate the distribution patterns of bike-sharing in station service areas, which are influenced not only by railway-station ridership but also by the built environment around the station, illustrating obvious spatial heterogeneity. To this end, we established a geographic weighted regression (GWR) model to capture this feature considering the variables of passenger flow and the built environment. Using the data from bike-sharing in Beijing, China, we applied the GWR model to carry out a spatiotemporal characteristic analysis of the relationship between bike-sharing usage in railway-station service areas and its determinants, including the passenger flow in stations, land use, bus lines, and road-network characteristics. The influence of these factors on bike-sharing usage is quite different in time and space. For instance, bus lines are a competing mode of transport with bike-sharing in suburban areas but not in city centers, whereas industrial and residential areas could also heavily affect the bike-sharing demand as well as railway-station ridership. The results of this work can help facilitate the dynamic allocation of bike-sharing and increase the efficiency of this emerging mode of transport.

What factors influence ridership of station-based bike sharing and free-floating bike sharing at rail transit stations?

“…Thirdly, to the best of our knowledge, there is little work done on simultaneously exploring the characters of two bike sharing systems (SBBS and FFBS) as transfer modes to rail transit station areas in one city. The existing literature focuses on the integration of rail transit with a single bike sharing system, either SBBS (e.g., Ma et al, 2018;Martin & Shaheen, 2014;Nair et al, 2013;Yang et al, 2016) or FFBS (e.g., Lin et al, 2019;Wang et al, 2020;Zhou et al, 2018).…”

“…The integration of bike sharing and rail transit can strengthen the win-win benefit, which provides residents with more flexibility and travel choices (Gu et al, 2019a;Wang et al, 2020). As rail transit feeder modes, two bike sharing systems (i.e., SBBS and FFBS) may exhibit different characteristics of user demand due to their different rebalancing strategies (Ma et al, 2020;Pal & Zhang, 2017).…”

Using Data on Bike-Sharing System User Stopovers in Smart Tourism: A Case Study

“…carry out a spatiotemporal characteristic analysis of the relationship between the bike-sharing usage in railwaystation service areas and its determinants, including the passenger flow in stations, land use, bus lines and roadnetwork characteristics [28]. However, the literature review has not identified any in-depth analyses of city bike user stopovers to identify and delimit attractive public spaces.…”

Modelling of User Behaviour for Static Rebalancing of Bike Sharing System: Transfer of Demand from Bike-Shortage Stations to Neighbouring Stations

“…eir objectives are to reduce congestion, gas emissions, and noise and to offer a flexible and cheap means of transport while having a beneficial effect on the health of users [6]. ey allow users to make relatively short journeys, of the order of 2.5 km [7] while integrating with other modes of transport in the intermodality context [8]. In addition, these systems provide a flexible mode of transport that improves "first mile/last mile" connections [9]; this distance is considered too long to walk between home and public transport and/or public transport and the workplace [10].…”