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Understanding crowd behaviors in a large social event is crucial for eventmanagement. Passive WiFi sensing, by collecting WiFi probe requests sent frommobile devices, provides a better way to monitor crowds compared with peoplecounters and cameras in terms of free interference, larger coverage, lowercost, and more information on people's movement. In existing studies, however,not enough attention has been paid to the thorough analysis and mining ofcollected data. Especially, the power of machine learning has not been fullyexploited. In this paper, therefore, we propose a comprehensive data analysisframework to fully analyze the collected probe requests to extract three typesof patterns related to crowd behaviors in a large social event, with the helpof statistics, visualization, and unsupervised machine learning. First,trajectories of the mobile devices are extracted from probe requests andanalyzed to reveal the spatial patterns of the crowds' movement. Hierarchicalagglomerative clustering is adopted to find the interconnections betweendifferent locations. Next, k-means and k-shape clustering algorithms areapplied to extract temporal visiting patterns of the crowds by days andlocations, respectively. Finally, by combining with time, trajectories aretransformed into spatiotemporal patterns, which reveal how trajectory durationchanges over the length and how the overall trends of crowd movement changeover time. The proposed data analysis framework is fully demonstrated usingreal-world data collected in a large social event. Results show that one canextract comprehensive patterns from data collected by a network of passive WiFisensors.

Understanding Crowd Behaviors in a Social Event by Passive WiFi Sensing and Data Mining