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Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing
Zhitao Li1, Yuzhen Shang1, Guanwei Zhao1,2
1School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China.
International Journal of Environmental Research and Public Health
|February 25, 2022
Summary
Dockless bike-sharing integrated with metro use is influenced by urban factors like CBD distance and POI density. Spatial analysis reveals varying impacts across Beijing, aiding urban planning for better integration.
Area of Science:
- Urban planning and transportation science
- Geographic Information Systems (GIS) and spatial analysis
- Sustainable urban mobility
Background:
- Dockless bike-sharing systems enhance urban mobility and extend metro service areas.
- Understanding the spatial relationship between urban built environments and shared mobility is crucial for effective planning.
- Integrating bike-sharing with public transit like metros offers a sustainable transportation solution.
Purpose of the Study:
- To investigate the spatial relationships between urban built environment factors and dockless bike-sharing usage connected to the metro.
- To compare the effectiveness of Ordinary Least Square (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models in analyzing these relationships.
- To identify key built environment factors influencing integrated bike-sharing and metro usage in Beijing.
Main Methods:
- Application of Ordinary Least Square (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models.
- Spatial analysis of dockless bike-sharing usage data in Beijing, China.
- Inclusion of built environment variables such as distance to CBD, POI density, road density, and population density.
Main Results:
- The MGWR model provided a more precise explanation of spatial relationships compared to OLS and GWR.
- Distance to the Central Business District (CBD), Hotels-Residences Points of Interest (POI) density, and road density were significant factors influencing integrated usage.
- Population density's effect was significant only on weekends, and the influence of built environment variables varied spatially, being more pronounced in the eastern study area.
Conclusions:
- The MGWR model effectively captures the complex spatial heterogeneity in the relationship between the built environment and metro-oriented dockless bike-sharing usage.
- Urban planning strategies should consider specific built environment factors and their spatial variations to optimize bike-sharing integration with metro systems.
- Findings support the development of bike-sharing-friendly urban environments to enhance the synergy between different transport modes.
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