DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction (Extended abstract)

Renhe Jiang1; Zekun Cai2; Zhaonan Wang2; Chuang Yang2; Zipei Fan2; Quanjun Chen2; Kota Tsubouchi3; Xuan Song1; Ryosuke Shibasaki2

10.1109/ICDE53745.2022.00136

2022

38th IEEE International Conference on Data Engineering (ICDE)

1063-6382

978-1-6654-0884-4

2022 IEEE 38th International Conference on Data Engineering (ICDE)

1519-1520

9-12 May 2022

Kuala Lumpur, Malaysia

10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA

IEEE COMPUTER SOC

Predicting the density and flow of the crowd at a citywide level is significant for city management. 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, 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. Compared with the existing ones, our dataset has larger mesh-grid number, finer-grained mesh size, and higher user sample. Towards such kind of 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. Both the datasets and codes are made available at https://github.com/deepkashiwa20/DeepCrowd.

Crowd Density Crowd Flow Human Mobility Open Data Pyramid ConvLSTMs Deep Learning

第一

英语

CPCI-S

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Theory & Methods

WOS:000855078401060

Web of Science

1.Department of Computer Science and Engineering, Southern University of Science and Technology
2.Center for Spatial Information Science, University of Tokyo
3.Yahoo Japan Corporation

Renhe Jiang,Zekun Cai,Zhaonan Wang,et al. DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction (Extended abstract)[C]. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA:IEEE COMPUTER SOC,2022:1519-1520.