Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System

Jiang, Renhe1,2; Cai, Zekun1; Wang, Zhaonan1; Yang, Chuang1; Fan, Zipei1,2; Chen, Quanjun1,2; Song, Xuan2; Shibasaki, Ryosuke1

2022-02-01

10.1145/3472300

ACM Transactions on Intelligent Systems and Technology   影响因子和分区

2157-6904

2157-6912

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.

Crowd management ubiquitous and mobile computing deep learning application and system

SCI ; EI

英语

通讯

Japan Society for the Promotion of Science (JSPS)["20K19859","20K19782"]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems

WOS:000784457600003

ASSOC COMPUTING MACHINERY

Web of Science

1.Univ Tokyo, Tokyo, Japan
2.Southern Univ Sci & Technol, SUSTech UTokyo Joint Res Ctr Super Smart City, Shenzhen, Peoples R China

Jiang, Renhe,Cai, Zekun,Wang, Zhaonan,et al. Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System[J]. ACM Transactions on Intelligent Systems and Technology,2022,13(2).

Jiang, Renhe.,Cai, Zekun.,Wang, Zhaonan.,Yang, Chuang.,Fan, Zipei.,...&Shibasaki, Ryosuke.(2022).Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System.ACM Transactions on Intelligent Systems and Technology,13(2).

Jiang, Renhe,et al."Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System".ACM Transactions on Intelligent Systems and Technology 13.2(2022).