GCN model combined with Bi-GRU for traffic prediction

Mu Bin; Zhen Lin

doi:10.1117/12.2641062

17 May 2022 GCN model combined with Bi-GRU for traffic prediction

Mu Bin, Zhen Lin

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 122592F (2022) https://doi.org/10.1117/12.2641062
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 2022, Kunming, China

Abstract

The acceleration of the urbanization process puts a higher demand on traffic management. In the last few years, ITS(intelligent transportation systems) have played a significant role, and traffic prediction is an important content among them. In fact, traffic forecasting is very challenging, mainly due to the temporal-spatial nature of the related traffic data. For this article, we propose an optimization method of graph convolutional neural network with Bi-GRU to optimize traffic prediction. Experiments show that our model has achieved good results and provides a new perspective for studying such problems.

Citation Download Citation

Mu Bin and Zhen Lin "GCN model combined with Bi-GRU for traffic prediction", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 122592F (17 May 2022); https://doi.org/10.1117/12.2641062

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