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Technol."],"published-print":{"date-parts":[[2022,4,30]]},"abstract":"<jats:p>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\u2019s 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 \u201cdeep\u201d 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.<\/jats:p>","DOI":"10.1145\/3472300","type":"journal-article","created":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T13:05:58Z","timestamp":1646658358000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System"],"prefix":"10.1145","volume":"13","author":[{"given":"Renhe","family":"Jiang","sequence":"first","affiliation":[{"name":"The University of Tokyo, SUSTech-UTokyo Joint Research Center on Super Smart City, Southern University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zekun","family":"Cai","sequence":"additional","affiliation":[{"name":"The University of Tokyo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaonan","family":"Wang","sequence":"additional","affiliation":[{"name":"The University of Tokyo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuang","family":"Yang","sequence":"additional","affiliation":[{"name":"The University of Tokyo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zipei","family":"Fan","sequence":"additional","affiliation":[{"name":"The University of Tokyo, SUSTech-UTokyo Joint Research Center on Super Smart City, Southern University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quanjun","family":"Chen","sequence":"additional","affiliation":[{"name":"The University of Tokyo, SUSTech-UTokyo Joint Research Center on Super Smart City, Southern University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Song","sequence":"additional","affiliation":[{"name":"SUSTech-UTokyo Joint Research Center on Super Smart City, Southern University ofScience and Technology, The University of Tokyo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryosuke","family":"Shibasaki","sequence":"additional","affiliation":[{"name":"The University of Tokyo"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,3,7]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. 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