論文誌 (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)
IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE)
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.