A method for traffic network speed forecasting based on GCN-BiLSTM

Wei Zhang; Bo Su; Pan Yang; Yiling Hu

doi:10.1117/12.3011491

7 December 2023 A method for traffic network speed forecasting based on GCN-BiLSTM

Wei Zhang, Bo Su, Pan Yang, Yiling Hu

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129414H (2023) https://doi.org/10.1117/12.3011491
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Accurate prediction results in the field of intelligent transportation systems are crucial. They enable commuters to make informed decisions regarding travel modes, routes, and departure times, which can significantly enhance travel efficiency. Due to the unique characteristics of road networks, accurate prediction is particularly important. This paper proposes a deep learning method combining graph convolutional neural network and bidirectional long short-term memory network. The model uses GCN to extract the topological structure features of the road network, learns the spatial correlation of the road, and uses BiLSTM to learn the temporal correlation of the road network speed data. Finally, non-traffic factors are integrated into the deep model to improve the accuracy of prediction. It is verified by real road network speed data. Experimental results show that compared with the traditional prediction model, the root mean square error of the GCN-BiLSTM combination model is 1.455, the mean absolute error is 1.283, the R2 of the coefficient of determination is 0.937, and the 95% confidence interval is [-2.866, 3.233], which are better than other models. The proposed model has higher accuracy and stability.

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

Citation Download Citation

Wei Zhang, Bo Su, Pan Yang, and Yiling Hu "A method for traffic network speed forecasting based on GCN-BiLSTM", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129414H (7 December 2023); https://doi.org/10.1117/12.3011491

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KEYWORDS

Roads

Data modeling

Matrices

Deep learning

Convolutional neural networks

Data acquisition

Feature extraction

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