An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai, China
Journal article, 2020

Revealing dockless bike-sharing utilization pattern and its explanatory factors are essential for urban planners and operators to improve the utilization and turnover of public bikes. This study explores the dockless bike-sharing utilization pattern from the perspective of bike using GPS-based bike origin-destination data collected in Shanghai, China. In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization. We then measure the utilization efficiency of bikes within each sub-region by calculating Time to booking (ToB) for each bike and explore how the built environment and social-demographic characteristics influence the bike-sharing utilization with ordinary least squares (OLS) regression and geographically weighted regression (GWR) models. The matrix factorization results indicate that the shared bikes mainly serve a certain area instead of the whole city. In addition, the GWR model shows higher explanatory power (Adjusted R2 = 0.774) than the OLS regression model (Adjusted R2 = 0.520), which suggests a close relationship between bike-sharing utilization and the selected explanatory variables. The coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area. This study can shed light on understanding the demand and supply of shared bikes for rebalancing and provide support for operators to improve the dockless bike-sharing utilization efficiency.

Social-demographic characteristics

Empirical analysis

Dockless bike-sharing

GPS bike origin-destination data

GWR

Built environment

Author

Aoyong Li

Swiss Federal Institute of Technology in Zürich (ETH)

Pengxiang Zhao

Swiss Federal Institute of Technology in Zürich (ETH)

Yizhe Huang

Shanghai Jiao Tong University

Kun Gao

Chalmers, Architecture and Civil Engineering, GeoEngineering

Kay W. Axhausen

Swiss Federal Institute of Technology in Zürich (ETH)

Journal of Transport Geography

0966-6923 (ISSN)

Vol. 88 102828

Subject Categories

Other Computer and Information Science

Transport Systems and Logistics

Economic Geography

DOI

10.1016/j.jtrangeo.2020.102828

More information

Latest update

9/14/2020