GCSTN: traffic flow prediction based on graph convolutional networks and spatiotemporal networks

Ziyi Cheng; JinHua Wang

doi:10.1117/12.3011258

8 November 2023 GCSTN: traffic flow prediction based on graph convolutional networks and spatiotemporal networks

Ziyi Cheng, JinHua Wang

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 1292319 (2023) https://doi.org/10.1117/12.3011258
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

Mitigating high-speed traffic congestion and predicting traffic flow have become major challenges in urban development. Most existing prediction methods rely on graph neural networks and attention mechanisms, but their central ideas focus solely on spatial or temporal features without effectively integrating spatiotemporal features for prediction. In this paper, we propose utilizing the Maximum Information Coefficient (MIC) to explore the relationship between temporal and spatial features in traffic data. We present two approaches: Correlation Attention Mechanism (COATT) and Spatiotemporal Maximum Information Coefficient (STMIC). COATT effectively incorporates spatial features embedded in traffic flow information into the graph convolution matrix, while adding a multi-head attention component ensures attention weights are well-calibrated and the influence of noise can be minimized and its impact reduced. By leveraging STMIC, the spatiotemporal matrix is combined to establish more accurate spatiotemporal dependencies in traffic flow. Experimental results on the high-speed highway traffic flow datasets (PEMS07 and PEMS08) demonstrate that our proposed GCSTN outperforms current state-of-the-art methods in traffic flow prediction. Particularly on the PEMS08 dataset, compared to ASTGCN, our model achieved a reduction of over 7.2% in MAE, RMSE, and MAPE metrics, while compared to STGODE, a reduction of over 3.5% in the same metrics.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ziyi Cheng and JinHua Wang "GCSTN: traffic flow prediction based on graph convolutional networks and spatiotemporal networks", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 1292319 (8 November 2023); https://doi.org/10.1117/12.3011258

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