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

doi:10.1007/s11704-019-9034-z

Front. Comput. Sci.

2020, Vol. 14

Issue (5) : 145310 https://doi.org/10.1007/s11704-019-9034-z

RESEARCH ARTICLE

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

Zhihan JIANG, Yan LIU, Xiaoliang FAN, Cheng WANG, Jonathan LI, Longbiao CHEN(

)

Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Information Science and Engineering, Xiamen University, Xiamen 361005, China

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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.

Keywords vehicle mobility big data spatial clustering event detection urban computing ubiquitous computing

Corresponding Author(s): Longbiao CHEN

Just Accepted Date: 12 July 2019 Issue Date: 10 March 2020

Cite this article:

Zhihan JIANG,Yan LIU,Xiaoliang FAN, et al. Understanding urban structures and crowd dynamics leveraging large-scale vehicle mobility data[J]. Front. Comput. Sci., 2020, 14(5): 145310.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-019-9034-z
https://academic.hep.com.cn/fcs/EN/Y2020/V14/I5/145310

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