Sensing Urban Structures and Crowd Dynamics with Mobility Big Data.

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.