Modelling and application of a spectral clustering method for shared bicycle trajectories

Abstract

Geographic flow clustering analysis can effectively reveal human behavioral patterns in movement. Traditional methods for studying human movement patterns are mostly based on first-order quantity analyses of point data, such as hotspots, density or clustering. Currently, relatively few second-order spatial analysis methods based on geographic flows exist. Thus, we developed a new geographic flow method based on spectral clustering and applied it to trajectory data analysis. This article uses the bike-sharing trajectories data in Shanghai in August 2016, spectral clustering analysis was conducted on the group flow patterns before, during and after rainfall, on weekdays and weekends and in the morning and evening peak. Spectral clustering was verified to exhibit better clustering effect by comparing the clustering indices of different clustering methods. This study enriches the analysis method of geographical flows, and the human mobility patterns revealed by its analysis can provide references for formulating urban green travel policies.

Keywords:

• Geographical flow
• spectral clustering
• bike-sharing trajectories
• human mobility patterns

Acknowledgements

The data in this study were obtained from publicly available data in Wenwen Li’s article, ‘Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories’, and we would like to thank the authors of this article for providing the data. The support of Cheng Zhou for the code of this study is also appreciated.

Disclosure statement

No potential conflict of interest was reported by the authors.

Author contributions

Wenwen Xing and Youjun Tu contributed equally to this article. Writing – original draft, Visualization, Writing – review and editing, Wenwen Xing; Methodology and Software, Youjun Tu; Data curation Yuxing Gao; Funding acquisition, Supervision AND Conceptualization, Junli Li and Zongyi He; Funding acquisition, Junli Li.

Additional information

Funding

This work was supported by the Key Natural Science Research Project of Higher Education Institutions in Anhui Province (No. 2023AH051013). The article was also supported by Natural Science Foundation of Anhui Province (No. 2108085MD129), and the Anhui New Era Education Quality Engineering Project (Graduate Education) (No. 2022zyxwjxalk039) and the Education Quality Engineering Project of Anhui Province (No. 2021xxkc038).