Traffic flow prediction based on adaptive graph convolutional recurrent network

Shuang Li

doi:10.1117/12.3034784

11 July 2024 Traffic flow prediction based on adaptive graph convolutional recurrent network

Shuang Li

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132101G (2024) https://doi.org/10.1117/12.3034784
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Forecasting traffic flow is a vital component in driving the advancement of modern cities and establishing intelligent transportation systems. The existing graph modeling methods have two limitations: the static spatial modeling cannot capture the fine-grained relationship between nodes, and the long-term prediction ability is poor. Aiming at these two limitations, an adaptive graph convolutional recurrent network (Ada-GCRN) is proposed. The main core is as follows: the adaptive graph structure learning component combines GCN to improve the extraction of spatial features, and the Transformer-like layer is introduced to capture global time dependence to improve the ability of long-term prediction.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuang Li "Traffic flow prediction based on adaptive graph convolutional recurrent network", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132101G (11 July 2024); https://doi.org/10.1117/12.3034784

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