{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepcrowd-a-deep-model-for-large-scale","title":"DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction","arxiv_id":null,"date":"2021-05-03","proceeding":"IEEE Transactions on Knowledge and Data Engineering 2021 5","authors":["Renhe Jiang","Zekun Cai","Zhaonan Wang","Chuang Yang","Zipei Fan","Quanjun Chen","Kota Tsubouchi","Xuan Song","Ryosuke Shibasaki"],"abstract":"Predicting the density and flow of the crowd or traffic at a citywide level becomes possible by using the big data and\r\ncutting-edge AI technologies. It has been a very significant research topic with high social impact, which can be widely applied to\r\nemergency management, traffic regulation, and urban planning. In particular, by meshing a large urban area to a number of\r\nfine-grained mesh-grids, citywide crowd and traffic information in a continuous time period can be represented with 4D tensor\r\n(Timestep, Height, Width, Channel). Based on this idea, a series of methods have been proposed to address grid-based prediction for\r\ncitywide crowd and traffic. In this study, we revisit the density and in-out flow prediction problem and publish a new aggregated human\r\nmobility dataset generated from a real-world smartphone application. Comparing with the existing ones, our dataset holds several\r\nadvantages including large mesh-grid number, fine-grained mesh size, and high user sample. Towards this large-scale crowd dataset,\r\nwe propose a novel deep learning model called DeepCrowd by designing pyramid architectures and high-dimensional attention\r\nmechanism based on Convolutional LSTM. Lastly, thorough and comprehensive performance evaluations are conducted to\r\ndemonstrate the superiority of the proposed DeepCrowd comparing to multiple state-of-the-art methods.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9422199","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9422199","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepcrowd-a-deep-model-for-large-scale","repo_url":"https://github.com/deepkashiwa20/DeepCrowd","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}