Abstract
In this paper, we present a new mobile sensing scheme to extract spatiotemporal information from citizens’ Wi-Fi CL (Connection Log) data, and provide a unified suspicious scoring algorithm to score and detect anomalous Crowd aggregation in heavily populated metropolitan areas. Smart phones could be city’s sensors to generate these CL data when their owners having access to Wi-Fi hotspot, meanwhile, wireless network transfer them in real-time back to the datacenter server. Totally, CL dataset includes 900 million sessions over 27 million users connecting to 28 million Wi-Fi hotspots within one month. We formulated Crowd detection problem and we carried out extensive experiments on CL dataset collected from four big cities in China, namely Beijing, Shanghai, Guangzhou, and Shenzhen. The suspicious score algorithm could reflect the density of Crowd, lasting period, and area coverage even with the same density of people. The experiments results are reported based on their suspicious scores. As the most crowded places, airports always get the highest marks, and shopping malls and tourist locations follow them. We believe these results are very useful for smart city safety maintenance or preventing tragedy events, etc.
The primary author of this work is a registered student.
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Jiang, L., Sun, L., Hwang, K. (2022). Anomalous Crowd Detection with Mobile Sensing and Suspicious Scoring. In: Liao, X., et al. Big Data. BigData 2022. Communications in Computer and Information Science, vol 1496. Springer, Singapore. https://doi.org/10.1007/978-981-16-9709-8_17
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DOI: https://doi.org/10.1007/978-981-16-9709-8_17
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