Traffic flow prediction based on graph convolutional networks: a survey

Jiawei Wang; Bin Ren; Chunhong He

doi:10.1117/12.3050973

20 January 2025 Traffic flow prediction based on graph convolutional networks: a survey

Jiawei Wang, Bin Ren, Chunhong He

Author Affiliations +

Proceedings Volume 13422, Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024); 1342217 (2025) https://doi.org/10.1117/12.3050973
Event: Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024), 2024, Xi'an, China

Abstract

Traffic flow prediction plays a vital role in modern intelligent transportation systems. In fact, predicting the future state of traffic is a difficult task because of the complexity of spatial relationships and temporal dependencies. In recent years, graph convolutional networks have been widely used in traffic flow prediction tasks and have shown excellent prediction performance. Therefore, prediction methods based on graph convolutional networks and their variants are key research directions in the field of traffic prediction. In this paper, we firstly review the development of graph convolutional networks in recent years, and secondly categorize graph convolutional networks and their variants into two main groups, and subdivided into nine types on this basis. In addition, representative graph convolutional networks and their variant models were selected for in-depth analysis and discussion. Finally, based on the current development status, the future research directions of traffic flow prediction technology based on graph convolutional networks are openly discussed.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiawei Wang, Bin Ren, and Chunhong He "Traffic flow prediction based on graph convolutional networks: a survey", Proc. SPIE 13422, Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024), 1342217 (20 January 2025); https://doi.org/10.1117/12.3050973

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