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Without sufficient preparation and on-site management, the mass scaleunexpected huge human crowd is a serious threat to public safety. A recentimpressive tragedy is the 2014 Shanghai Stampede, where 36 people were killedand 49 were injured in celebration of the New Year's Eve on December 31th 2014in the Shanghai Bund. Due to the innately stochastic and complicated individualmovement, it is not easy to predict collective gatherings, which potentiallyleads to crowd events. In this paper, with leveraging the big data generated onBaidu map, we propose a novel approach to early warning such potential crowddisasters, which has profound public benefits. An insightful observation isthat, with the prevalence and convenience of mobile map service, users usuallysearch on the Baidu map to plan a routine. Therefore, aggregating users' querydata on Baidu map can obtain priori and indication information for estimatingfuture human population in a specific area ahead of time. Our careful analysisand deep investigation on the Baidu map data on various events alsodemonstrates a strong correlation pattern between the number of map query andthe number of positioning users in an area. Based on such observation, wepropose a decision method utilizing query data on Baidu map to invoke warningsfor potential crowd events about 1-3 hours in advance. Then we also construct amachine learning model with heterogeneous data (such as query data and mobilepositioning data) to quantitatively measure the risk of the potential crowddisasters. We evaluate the effectiveness of our methods on the data of Baidumap.

Early Warning of Human Crowds Based on Query Data from Baidu Map: Analysis Based on Shanghai Stampede