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Sensing Urban Structures and Crowd Dynamics with Mobility Big Data

  • Yan Liu
  • Longbiao ChenEmail author
  • Linjin Liu
  • Xiaoliang Fan
  • Sheng Wu
  • Cheng Wang
  • Jonathan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

Abstract

To facilitate efficient and effective city management, it is important for urban authorities to understand the regular functionalities of urban areas and the irregular crowd dynamics moving around the city. However, existing methods relying on manual surveys and statistics usually cost substantial time and labor, hindering the fine-grain characterization of urban structures and the in-depth understanding of crowd dynamics. In this paper, we leverage large-scale mobility data collected from vehicle GPS devices to analyze the dynamics of crowd movement in different urban areas in a low-cost and automatic manner. We extract the regular crowd movement patterns in different areas, detect the abnormal crowd movement flow peaks, and then interpret the influences of different types of urban events. More specifically, we first divide the city into fine-grained geographic regions and cluster them according to the similarity of crowd movement characteristics. Second, we detect anomaly traffic flow for each cluster area, interpret urban events for each abnormal flow point, and correlate urban events to the interpretation results. Finally, we determine the scope of urban events and use visualization techniques to demonstrate the impact of different types of urban events. We leverage the large-scale real-world datasets from Xiamen City for evaluation. Experimental results validate the effectiveness of our method, and several case studies in Xiamen are conducted.

Keywords

Crowdsensing Mobility big data Urban computing 

Notes

Acknowledgment

We would like to thank the reviewers for their constructive suggestions. This research was supported by Fujian Collaborative Innovation Center for Big Data Applications in Governments, and the China Fundamental Research Funds for the Central Universities No. 0630/ZK1074, and NSF of China No. U1605254, No. 61371144, No. 61300232.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yan Liu
    • 1
  • Longbiao Chen
    • 1
    Email author
  • Linjin Liu
    • 1
  • Xiaoliang Fan
    • 1
  • Sheng Wu
    • 3
  • Cheng Wang
    • 1
  • Jonathan Li
    • 1
    • 2
  1. 1.Fujian Key Laboratory of Sensing and Computing for Smart CitiesXiamen UniversityXiamenChina
  2. 2.WatMos LabUniversity of WaterlooWaterlooCanada
  3. 3.Spatial Information Research Center of FujianFuzhou UniversityFuzhouChina

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