Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System
Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System
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DOI：
10.1145/3472300
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Renhe Jiang;Z. Cai;Zhaonan Wang;Chuang Yang;Z. Fan;Quanjun Chen;Xuan Song;R. Shibasaki
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文献类型：
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作者：
Renhe Jiang;Z. Cai;Zhaonan Wang;Chuang Yang;Z. Fan;Quanjun Chen;Xuan Song;R. Shibasaki
活动人群管理是一个重要的研究课题，具有很高的社会影响力。当一些重大事件发生时，如地震，台风和国家节日，人群管理成为政府的首要任务（例如，警察）和公共服务运营商（例如，地铁/巴士运营商），以保护人民的安全或维持公共基础设施的运作。然而，在这样的事件情况下，人的行为将变得与日常生活非常不同，这使得预测大型活动中的人群动态变得非常具有挑战性，特别是在城市范围内。因此，在这项研究中，我们的目标是提取“深”的趋势，只从当前的瞬间观察，并产生一个准确的预测趋势，在短期内，这被认为是一个有效的方式来处理事件的情况。受此启发，我们构建了一个名为DeepUrbanEvent的在线系统，该系统可以迭代地将当前一小时的全市人群动态作为输入，并报告下一个小时的预测结果作为输出。设计了一种使用递归神经网络构建的新型深度学习架构，以类似于视频预测任务的方式有效地对这些高度复杂的序列数据进行建模。实验结果表明，我们提出的方法优于现有的方法的上级性能。最后，我们将我们的原型系统应用于多个大型现实世界的事件，并表明它是高度可部署的在线人群管理系统。
Event crowd management has been a significant research topic with high social impact. When some big events happen such as an earthquake, typhoon, and national festival, crowd management becomes the first priority for governments (e.g., police) and public service operators (e.g., subway/bus operator) to protect people’s safety or maintain the operation of public infrastructures. However, under such event situations, human behavior will become very different from daily routines, which makes prediction of crowd dynamics at big events become highly challenging, especially at a citywide level. Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations. Motivated by these, we build an online system called DeepUrbanEvent, which can iteratively take citywide crowd dynamics from the current one hour as input and report the prediction results for the next one hour as output. A novel deep learning architecture built with recurrent neural networks is designed to effectively model these highly complex sequential data in an analogous manner to video prediction tasks. Experimental results demonstrate the superior performance of our proposed methodology to the existing approaches. Lastly, we apply our prototype system to multiple big real-world events and show that it is highly deployable as an online crowd management system.