DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction

Jiang，Renhe1; Cai，Zekun2; Wang，Zhaonan3; Yang，Chuang4; Fan，Zipei5; Chen，Quanjun6; Tsubouchi，Kota7; Song，Xuan8; Shibasaki，Ryosuke9

2021

10.1109/TKDE.2021.3077056

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING   影响因子和分区

1041-4347

1558-2191

Predicting the density and flow of the crowd or traffic at a citywide level becomes possible by using the big data and cutting-edge AI technologies. It has been a very significant research topic with high social impact, which can be widely applied to emergency management, traffic regulation, and urban planning. In particular, by meshing a large urban area to a number of fine-grained mesh-grids, citywide crowd and traffic information in a continuous time period can be represented with 4D tensor (Timestep, Height, Width, Channel). Based on this idea, a series of methods have been proposed to address grid-based prediction for citywide crowd and traffic. In this study, we revisit the density and in-out flow prediction problem and publish a new aggregated human mobility dataset generated from a real-world smartphone application. Comparing with the existing ones, our dataset holds several advantages including large mesh-grid number, fine-grained mesh size, and high user sample. Towards this large-scale crowd dataset, we propose a novel deep learning model called DeepCrowd by designing pyramid architectures and high-dimensional attention mechanism based on Convolutional LSTM. Lastly, thorough and comprehensive performance evaluations are conducted to demonstrate the superiority of the proposed DeepCrowd comparing to multiple state-of-the-art methods.

Data models Deep learning Global Positioning System Predictive models Public transportation Tensors Trajectory

EI ; SCI

英语

ESI高被引

其他

Japan Society for the Promo-tion of Science (JSPS)[20K19859]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic

WOS:000895445500021

IEEE COMPUTER SOC

20212010371220

Automobile radiators ; Continuous time systems ; Deep learning ; Forecasting ; Long short-term memory ; Mesh generation ; Risk management ; Traffic control ; Urban planning

Urban Planning and Development:403.1 ; Space Heating Equipment and Components:643.2 ; Computer Applications:723.5 ; Systems Science:961

ENGINEERING

2-s2.0-85105854733

Scopus

1.Information Technology Center; Center for Spatial Information Science, The University of Tokyo, 13143 Bunkyo-ku, Tokyo, Japan, (e-mail: jiangrh@csis.u-tokyo.ac.jp)
2.Center for Spatial Information Science, The University of Tokyo, 13143 Bunkyo-ku, Tokyo, Japan, (e-mail: caizekun@csis.u-tokyo.ac.jp)
3.Center for Spatial Information Science, The University of Tokyo, 13143 Bunkyo-ku, Tokyo, Japan, (e-mail: zn.wang@aist.go.jp)
4.Center for Spatial Information Science, The University of Tokyo, 13143 Bunkyo-ku, Tokyo, Japan, (e-mail: chuang.yang@csis.u-tokyo.ac.jp)
5.Center for Spatial Information Science, The University of Tokyo, 13143 Bunkyo-ku, Chiba, Japan, 113-0033 (e-mail: fanzipei@iis.u-tokyo.ac.jp)
6.Center for Spatial Information Science, The University of Tokyo, 13143 Bunkyo-ku, Tokyo, Japan, (e-mail: chen1990@iis.u-tokyo.ac.jp)
7.Yahoo Japan Research, Yahoo Japan Corporation, 385145 Chiyoda-ku, Tokyo-to, Japan, (e-mail: ktsubouc@yahoo-corp.jp)
8.SUSTech-UTokyo Joint Research Center on Super Smart City, Department of Computer Science and Engineering, Southern University of Science and Technology, 255310 Shenzhen, Guangdong, China, (e-mail: songxuan@csis.u-tokyo.ac.jp)
9.Center for Spatial Information Science, University of Tokyo, Tokyo, Tokyo, Japan, (e-mail: shiba@csis.u-tokyo.ac.jp)

Jiang，Renhe,Cai，Zekun,Wang，Zhaonan,et al. DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2021,PP(99):1-1.

Jiang，Renhe.,Cai，Zekun.,Wang，Zhaonan.,Yang，Chuang.,Fan，Zipei.,...&Shibasaki，Ryosuke.(2021).DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,PP(99),1-1.

Jiang，Renhe,et al."DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING PP.99(2021):1-1.