Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction

Ahmad Ali; Yanmin Zhu; Muhammad Zakarya

doi:10.1016/j.neunet.2021.10.021

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Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction

Ahmad Ali Yanmin Zhu Muhammad Zakarya

Year: 2021 Journal: Neural Networks Vol: 145 Pages: 233-247 Publisher: Elsevier BV

DOI: 10.1016/j.neunet.2021.10.021

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Keywords:

Computer science Scalability Granularity Graph Convolutional neural network Generalization Artificial intelligence Fuse (electrical) Data mining Theoretical computer science

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Traffic Prediction and Management Techniques

Physical Sciences → Engineering → Building and Construction

Human Mobility and Location-Based Analysis

Social Sciences → Social Sciences → Transportation

Traffic control and management

Physical Sciences → Engineering → Control and Systems Engineering

Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction

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