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Understanding urban structures and crowd dynamics leveraging large-scale vehicle mobility data

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Abstract

A comprehensive understanding of city structures and urban dynamics can greatly improve the efficiency and quality of urban planning and management, while the traditional approaches of which, such as manual surveys, usually incur substantial labor and time. In this paper, we propose a data-driven framework to sense urban structures and dynamics from large-scale vehicle mobility data. First, we divide the city into fine-grained grids, and cluster the grids with similar mobility features into structured urban areas with a proposed distance-constrained clustering algorithm (DCCA). Second, we detect irregular mobility traffic patterns in each area leveraging an ARIMA-based anomaly detection algorithm (ADAM), and correlate them to the urban social and emergency events. Finally, we build a visualization system to demonstrate the urban structures and crowd dynamics. We evaluate our framework using real-world datasets collected from Xiamen city, China, and the results show that the proposed framework can sense urban structures and crowd comprehensively and effectively.

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Acknowledgements

We would like to thank the reviewers for their constructive suggestions. This research was supported by the China Fundamental Research Funds for the Central Universities (20720170040), the National Natural Science Foundation of China (Grant No. 61802325), and Natural Science Foundation of Fujian Province, China (2018J01105).

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Correspondence to Longbiao Chen.

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Zhihan Jiang received the BSc degree in computer science and technology from Xiamen University, China in 2018. She is currently pursuing the master’s degree in Department of Computer Science, Xiamen University, China.

Yan Liu received the master’s degree in Department of Computer Science, Xiamen University, China in 2018. Her research interests include big data analytics and urban computing.

Xiaoliang Fan is a senior research engineer with Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, China. He received his PhD degree at University Pierre and Marie CURIE, France in 2012. His research interests include services computing, and big data analytics.

Cheng Wang received the PhD degree in information and communication engineering from the National University of Defense Technology, China in 2002. He is a professor with Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China. His research interests include remote sensing image processing, mobile LiDAR data analysis, and multisensor fusion.

Jonathan Li received the PhD degree in geomatics engineering from the University of Cape Town, South Africa in 2000. He is currently a professor with Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, China. He is also a professor and the Head of the WatMos Lab, Faculty of Environment, University of Waterloo, Canada. His current research interests include information extraction from LiDAR point clouds and from earth observation images.

Longbiao Chen is an assistant professor with Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, China. He obtained his PhD degree in computer science from Sorbonne University, France in 2018 and Zhejiang University, China in 2016. His research interests include Ubiquitous Computing, Urban Computing, and Big Data Analytics.

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Jiang, Z., Liu, Y., Fan, X. et al. Understanding urban structures and crowd dynamics leveraging large-scale vehicle mobility data. Front. Comput. Sci. 14, 145310 (2020). https://doi.org/10.1007/s11704-019-9034-z

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