Discovering urban mobility structure: a spatio-temporal representational learning approach

ABSTRACT

The urban mobility structure is a summary of individual movement patterns and the interaction between persons and the urban environment, which is extremely important for urban management and public transportation route planning. The majority of current research on urban mobility structure discovery utilizes the urban environment as a static network to detect the relationship between people groups and urban areas, ignoring the vital problem of how individuals affect urban mobility structure dynamically. In this paper, we propose a spatio-temporal representational learning method based on reinforcement learning for discovering urban mobility structures, in which the model can effectively consider the interaction knowledge graph of individuals with stations while accounting for the spatio-temporal heterogeneity of individual travel. The experimental results demonstrate the advantages of individual travel-based urban mobility structure discovery research in describing the interaction between individuals and urban areas, which can account for the intrinsic influence more thoroughly.

KEYWORDS:

• Urban mobility structure
• representational learning
• individual travel
• spatio-temporal heterogeneity

Acknowledgements

The authors would like to thank the data distribution agencies for providing the test data and prof. Youliang Tian for his guidance and financial support. The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University. Finally, the Authors would like to thank the Reviewers for their comments that helped to significantly improve the quality of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Key Research and Development Program of China [grant 2021YFB3101100], the National Natural Science Foundation of China [grant 62272123, 42371470], the Guizhou University Doctoral Fund [grant 20235019969], the Natural Science Foundation of Hunan Province, China [grant 2023JJ40100], the Scientific Research Fund of Hunan Provincial Education Department [grant 22A0498], and the Open fund project of National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns [grant 2021HSKFJJ015, CTCZ20K01].