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1. DeepCrowd: A Deep Model for Large
Scale Citywide Crowd Density and Flow
Prediction
Abstract
Predicting the density and flow of the crowd or traffic at a citywide level
becomes possible by using the big data and cutting
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 t
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,
revisit the density and in
aggregated human mobility dataset generated from a real
application. Comparing with the existing ones, our dataset holds several
advantages including large mesh
high user sample. Towards this large
DeepCrowd: A Deep Model for Large
Scale Citywide Crowd Density and Flow
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
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
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
based prediction for citywide crowd and traffic. In this study,
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
DeepCrowd: A Deep Model for Large-
Scale Citywide Crowd Density and Flow
Predicting the density and flow of the crowd or traffic at a citywide level
edge AI technologies. It
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-
raffic 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
based prediction for citywide crowd and traffic. In this study, we
out flow prediction problem and publish a new
world smartphone
application. Comparing with the existing ones, our dataset holds several
grained mesh size, and
scale crowd dataset, we propose a

2. 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.