Overview of Traffic Flow Prediction Research Based on Graph Neural Networks

DOI

10.2991/978-94-6239-648-7_78How to use a DOI?

Keywords

Traffic flow prediction; Graph neural network; God often uses differential equations; Spatiotemporal data mining; Attention mechanism

Abstract

The acceleration of global urbanization and the continuous increase of the amount of vehicles have made traffic jams a severe challenge faced by cities around the world. How to accurately and real-time predict traffic flow has become a core element in building intelligent transportation systems. To better alleviate traffic congestion, improve travel efficiency, and ensure road safety, graph neural networks have begun to develop rapidly and are widely used in traffic flow prediction. With its powerful non-Euclidean data modeling capabilities, it has shown enormous potential in the field of traffic flow prediction. This review studies the latest progress in predicting traffic flow based on graph neural network methods from 2024 to 2025 and summarizes the mainstream methods in this field based on three different bases. It also lists three commonly used datasets and the evaluation criteria for datasets in this field and compares and analyzes the performance of the models. Then, the limitations of existing methods were pointed out, and future research directions and solutions were discussed to provide reference for the development of this field.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yi Shao
PY  - 2026
DA  - 2026/04/24
TI  - Overview of Traffic Flow Prediction Research Based on Graph Neural Networks
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 722
EP  - 730
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_78
DO  - 10.2991/978-94-6239-648-7_78
ID  - Shao2026
ER  -