Advancements of Graph Neural Networks in Urban Traffic Prediction

Jiangnan Liu, Shipeng Xu

2024

Abstract

Accurate traffic flow and travel speed prediction are essential for intelligent transportation systems. A particular kind of deep learning model called Graph Neural Networks (GNNs) is made especially to deal with graph-structured data, such as road networks. Road networks include intricate relationships and dependencies that can be precisely captured by them, which makes them an excellent choice for large-scale traffic flow and journey speed prediction. The use of GNNs in predicting urban traffic is reviewed in this work. We focus on methods for addressing spatiotemporal dependencies, including Spatiotemporal Graph Neural Networks (S-GNNs), Temporal Graph Convolutional Networks (T-GCNs), and attention-based techniques. Furthermore, we discuss Deep GNNs for enhancing traffic prediction accuracy, as well as the application of GNNs combined with the Internet of Things (IoT) for emergency traffic planning. Despite the substantial potential of GNNs in traffic prediction, there is a lack of systematic exploration and comprehensive analysis regarding their applicability in diverse urban environments, the optimization of real-time prediction capabilities, and their integration with urban planning and management strategies.

Paper Citation

in Harvard Style

Liu J. and Xu S. (2024). Advancements of Graph Neural Networks in Urban Traffic Prediction. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 62-66. DOI: 10.5220/0012902200004508

in Bibtex Style

@conference{emiti24,
author={Jiangnan Liu and Shipeng Xu},
title={Advancements of Graph Neural Networks in Urban Traffic Prediction},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={62-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012902200004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Advancements of Graph Neural Networks in Urban Traffic Prediction
SN - 978-989-758-713-9
AU - Liu J.
AU - Xu S.
PY - 2024
SP - 62
EP - 66
DO - 10.5220/0012902200004508
PB - SciTePress