COMPARING THE SPATIOTEMPORAL TRAVEL PATTERNS AND INFLUENCING FACTORS OF BIKE SHARING AND E-BIKE SHARING SYSTEMS

Abstract. The emergence of micromobility, exemplified by bike sharing and e-bike sharing systems, has ushered in a low-carbon, environmentally friendly, and sustainable revolution in urban transportation. This transformative shift addresses the "first and last mile" challenge and holds immense potential for urban mobility enhancement. Nevertheless, the existing literature predominantly investigates the spatiotemporal travel patterns and influencing factors of bike sharing and e-bike sharing systems in isolation, overlooking comparative analyses grounded in quantitative methodologies. In order to fill this gap, this study first compares and analysis their spatiotemporal travel patterns, which are measured by travel distance, travel time, and travel volume. A Multiscale Geoweighted Regression (MGWR) model was constructed using various data sources, such as Point of Interest (POI) data, metro station data, and bus stop data, to conduct a spatiotemporal correlation analysis of land use and public transport factors with the travel volume of the shared system. Our study centers on Manhattan, New York City, utilizing data from May 2022 for both bike sharing and e-bike sharing systems. The study analysis reveals that hourly trip volumes are higher for bike sharing than for e-bike sharing, exhibiting substantial spatial variation across different regions within the city. The MGWR model's findings suggest that educational facilities exert a negative influence on bike sharing in the northeast and on e-bike sharing trips in the central region, with this impact being more pronounced on weekends. Similarly, cultural facilities negatively affect the Central region's bike sharing system and the citywide e-bike sharing system, with a milder effect during weekends. Moreover, bus stops exhibit a significant negative impact on bike sharing and e-bike sharing at Chelsea Waterside Park (only weekdays), while displaying a positive influence on both systems during weekends. To validate the MGWR model's efficacy, we conducted a comparative analysis with a Geographically Weighted Regression (GWR) model. The results demonstrate that MGWR can be more effective in correlating and quantitatively explaining the effects of different factors on spatiotemporal travel patterns. In conclusion, this study furnishes valuable insights for optimizing urban infrastructure rebalancing strategies and advancing sustainable urban infrastructure development.