Publications
カンファレンス (国際) DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction
Renhe Jiang (the University of Tokyo), Zekun Cai (the University of Tokyo), Zhaonan Wang (the University of Tokyo), Chuang Yang (the University of Tokyo), Zipei Fan (the University of Tokyo), Quanjun Chen (the University of Tokyo), Kota Tsubouchi, Xuan Song (the University of Tokyo), Ryosuke Shibasaki (the University of Tokyo)
The 38th IEEE International Conference on Data Engineering (ICDE22)
2022.5.11
Predicting the density and flow of the crowd at a citywide level is significant for emergency management, traffic regulation, and urban planning. 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 largescale crowd dataset, we propose a novel deep learning model called DeepCrowd by designing pyramid architectures and highdimensional attention mechanism based on Convolutional LSTM. Both the datasets and codes are made available at https://github. com/deepkashiwa20/DeepCrowd.