Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing Usage.

Zhitao Li,Guanwei Zhao,Muzhuang Yang,Yuzhen Shang

doi:10.3390/ijerph19042323

Abstract

Dockless bike-sharing systems have become one of the important transport methods for urban residents as they can effectively expand the metro’s service area. We applied the ordinary least square (OLS) model, the geographically weighted regression (GWR) model and the multiscale geographically weighted regression (MGWR) model to capture the spatial relationship between the urban built environment and the usage of bike-sharing connected to the metro. A case study in Beijing, China, was conducted. The empirical result demonstrates that the MGWR model can explain the varieties of spatial relationship more precisely than the OLS model and the GWR model. The result also shows that, among the proposed built environment factors, the integrated usage of bike-sharing and metro is mainly affected by the distance to central business district (CBD), the Hotels-Residences points of interest (POI) density, and the road density. It is noteworthy that the effect of population density on dockless bike-sharing usage is only significant at weekends. In addition, the effects of the built environment variables on dockless bike-sharing usage also vary across space. A common feature is that most of the built environment factors have a more obvious impact on the metro-oriented dockless bike-sharing usage in the eastern part of the study area. This finding can provide support for governments and urban planners to efficiently develop a bike-sharing-friendly built environment that promotes the integration of bike-sharing and metro.

Highlights

Each trip record includes order id, user id, vehicle id, vehicle type, In order to explore the spatial relationship between the built environment and the usage of bike-sharing connected to the metro in Beijing, the first step is to identify the bike-sharing trip used to connect to the metro station
It can be inferred that the built environment variables involved in this model have significant positive spatial autocorrelation between each other
In order to analyze the spatial heterogeneity of the impact of each built environment variable on the usage of bike-sharing connected to the metro, the local estimated coefficients of each variable in the multiscale geographically weighted regression (MGWR) model are shown in Figures 5–9, respectively

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Use of dockless bike-sharing has become more popular due to its advantages in terms of health, convenience, flexibility, and so on. Since they do not require docking stations, the biggest advantage of dockless bike-sharing is flexibility and convenience compared with the traditional docked bike-sharing system. Previous studies show that the dockless bike-sharing system changes the daily travel mode of urban residents constantly, especially providing an effective solution to the “first and last mile” travel problems for residents [1,2,3]. Cycling is proved to be beneficial to the environment as it can significantly reduce traffic’s energy consumption and carbon emissions [4,5,6]

Methods

Results

Discussion

Conclusion