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20 February 2024 Heterogeneous fusion graph neural network for traffic prediction
Di Zang, Juntao Lei
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Proceedings Volume 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023); 130640V (2024) https://doi.org/10.1117/12.3015704
Event: 7th International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 2023, Dalian, China
Abstract
Accurate urban traffic management relies on precise traffic predictions. Spatial-temporal graph neural networks, combining graph neural networks with time series processing, have gained popularity for traffic prediction. However, traditional graph neural networks fall short in modeling high-order interactions among multiple nodes. To address these challenges, we present a novel approach, the Heterogeneous Fusion Graph Neural Network (HFGNN). In our model, we integrate hypergraph convolution which can model higher-order relationships between nodes with graph convolution to capture more complex spatial dependencies in traffic network structure. Experiments on real-world traffic data confirm the superiority of our model over state-of-the-art methods.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Di Zang and Juntao Lei "Heterogeneous fusion graph neural network for traffic prediction", Proc. SPIE 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 130640V (20 February 2024); https://doi.org/10.1117/12.3015704
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KEYWORDS
Convolution
Neural networks
Data modeling
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