Spatiotemporal characteristics analysis of commuting by shared electric bike: A case study of Ningbo, China

Abstract

Understanding the spatiotemporal characteristics of shared e-bike commuting is crucial for promoting transit-oriented sustainable urban development. Shared e-bikes have been widely encouraged and supported by authorities around the world. However, studies on exploring commuting identification and demand factors for shared e-bikes are rare. Taking Ningbo as an example, this paper presents a commuting identification method for shared e-bikes based on the Density-based Spatial Clustering of Application with Noise (DBSCAN) algorithm. The geographically and temporally weighted regression model (GTWR) was constructed to reveal the spatiotemporal impacts of the built environment, transportation facilities, social economy and weather conditions on demand for shared e-bikes. The results show that schools, working population, parking lots and subway stations are positively correlated with the morning and evening rush hour demand in terms of time dimension. In the spatial dimension, the companies, living-service areas, schools and parking lots around subway stations, downtown and CBD are also positively correlated with the rush-hour demand. Rainfall and suburbs are negatively correlated with commuting demand. Low-income work areas, public parking lots around companies without a dedicated parking lot, and traffic congestion areas such as the city center are high-demand spots for shared e-bikes. When the subway station connection is promoted during rush hours, the demand for shared e-bikes in the parking lots is reduced. Off-peak commuting of enterprises also leads to a drop in demand for shared e-bikes. The results of this study can provide more comprehensive support for commuting management, essential policy significance for government agencies and shared e-bikes operators, and guiding significance for promoting sustainable urban development.