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Gravity model in dockless bike-sharing systems within cities
Ruiqi Li1, Shuai Gao1, Ankang Luo1
1UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
Physical Review. E
|February 19, 2021
Summary
Dockless bike-sharing data reveals urban biking traffic patterns. A spatial scaling relation and power law distribution were found, improving urban planning for sustainable development.
Area of Science:
- Urban planning
- Transportation science
- Data science
Background:
- Limited data on biking behaviors previously hindered urban planning.
- Dockless bike-sharing systems now offer rich data for studying urban cycling patterns.
Purpose of the Study:
- To analyze urban biking traffic patterns using dockless bike-sharing data.
- To identify spatial regularities and validate predictive models for cycling flows.
Main Methods:
- Analysis of spatial scaling relations between riding volume and distance to city center.
- Application of power law distributions to biking flows between locations.
- Validation of the gravity model for predicting urban biking traffic.
Main Results:
- A spatial scaling relation and power law distribution were identified in Beijing and Shanghai.
- The gravity model effectively predicts biking traffic at fine resolutions.
- Population-related parameters suggest smaller populations are more significant per capita for biking generation.
Conclusions:
- Dockless bike-sharing data provides valuable insights into urban cycling dynamics.
- Understanding these patterns is crucial for sustainable urban development and transportation planning.
- Further research is needed to explain anomalies in flow patterns for specific locations.
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