Efficient Spatio-Temporal Graph Neural Networks for Traffic Forecasting

Yackov Lubarsky; Alexei Gaissinski; Pavel Kisilev

doi:10.1007/978-3-031-34107-6_9

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Efficient Spatio-Temporal Graph Neural Networks for Traffic Forecasting

Yackov Lubarsky Alexei Gaissinski Pavel Kisilev

Year: 2023 IFIP advances in information and communication technology Pages: 109-120 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-34107-6_9

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Keywords:

Computer science Pooling Graph Time series Inference Data mining Dependency (UML) Artificial intelligence Machine learning Theoretical computer science

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Topics

Traffic Prediction and Management Techniques

Physical Sciences → Engineering → Building and Construction

Transportation Planning and Optimization

Social Sciences → Social Sciences → Transportation

Time Series Analysis and Forecasting

Physical Sciences → Computer Science → Signal Processing

Efficient Spatio-Temporal Graph Neural Networks for Traffic Forecasting

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