Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks

Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks

Being able to predict the crowd flows in each and every part of a city, especially in irregular regions, is strategically important for traffic control, risk assessment, and public safety. However, it is very challenging because of interactions and spatial correlations between different regions. In...

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Bibliographic Details
Main Authors: Li, Q. (Author), Liang, Y. (Author), Sun, J. (Author), Yi, X. (Author), Zhang, J. (Author), Zheng, Y. (Author)
Format: Article
Language:English
Published: IEEE Computer Society 2022
Subjects:
Convolution
Convolutional networks
Crowd flows
Flow forecasting
Flow graphs
Forecasting
Graphic methods
Multi-view learning
Multi-views
neural network
Neural-networks
Public safety
Risk assessment
Risks assessments
Spatial correlations
spatio-temporal prediction
Spatio-temporal prediction
Taxicabs
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