Interactive visual analytics of moving passenger flocks using massive smart card data

ABSTRACT

Understanding urban mobility patterns is constrained by our limited capabilities to extract and visualize spatio-temporal regularities from large amounts of mobility data. Moving flocks, defined as groups of people traveling along over a pre-defined time duration, can reveal collective moving patterns at aggregated spatio-temporal scales, thereby facilitating the discovery of urban mobility structure and travel demand patterns. In this study, we extend classical trajectory-oriented flock mining algorithms to discover moving flocks of transit passengers, accounting for the constraints of multi-modal transit networks. We develop a map-centered visual analytics approach by integrating the flock mining algorithm with interactive visualization designs of discovered flocks. Novel interactive visualizations are designed and implemented to support the exploration and analyses of discovered moving flocks at different spatial and temporal scales. The visual analytics approach is evaluated using a real-world smart card dataset collected in Shenzhen City, China, validating its applicability in capturing and mapping dynamic mobility patterns over a large metropolitan area.

KEYWORDS:

• Moving flock
• smart card data
• visual analytics
• mobility patterns

Disclosure statement

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

Data availability statement

The source code that support the findings of this study are available with the identifier(s) at the private link (https://figshare.com/s/416eb2f866a1fbf5f480). Trip data and station/stop GIS data cannot be made publicly available due to the restrictions in the agreement with the data provider.

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Additional information

Funding

This project was supported by National Natural Science Foundation of China under grant 41871308, National Key R&D Program of China (International Scientific & Technological Cooperation Program) under grant [2019YFE0106500], and the Fundamental Research Funds for the Central Universities.