Spatio-Temporal Traffic Prediction for Efficient ITS Management

Aram Nasser, Vilmos Simon

2025

Abstract

Traffic forecasting is a crucial element of Intelligent Transportation Systems (ITSs), exerting significant influence on the optimization of urban mobility. Through precise anticipation of traffic patterns, ITS facilitates proactive traffic flow management, leading to a multitude of benefits for both the city and its inhabitants. However, the intricate topological structure of road networks and the changing temporal patterns in traffic create challenging problems that demand solutions considering both the spatial and temporal aspects of traffic characteristics. Most existing traffic prediction models are influenced by Graph Neural Networks (GNNs) to capture the spatial structure of road networks. However, this approach typically relies on the adjacency matrix, which might not always reflect the dynamic state of traffic conditions. In addition, GNNs are not universally applicable across different traffic topologies. What works for one road network may not yield the same results for another, owing to disparities in the number of roads, thus graph nodes, and the unique characteristics of each location. Therefore, in this paper, the Spatio-Temporal Multi-Head Attention (ST-MHA) model is introduced to solve this issue. ST-MHA depends on a modified version of the Multi-Head Attention (MHA) mechanism to capture the spatial structure of the road network implicitly, as well as a GRU-based encoder-decoder structure for integrating the temporal characteristics. Our model outperforms three state-of-the-art baseline models, which include temporal, spatial, and spatio-temporal models. This enhanced performance is evident across three different prediction horizons when evaluated on a real-world traffic dataset.

Paper Citation

in Harvard Style

Nasser A. and Simon V. (2025). Spatio-Temporal Traffic Prediction for Efficient ITS Management. In Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-745-0, SciTePress, pages 45-53. DOI: 10.5220/0013229700003941

in Bibtex Style

@conference{vehits25,
author={Aram Nasser and Vilmos Simon},
title={Spatio-Temporal Traffic Prediction for Efficient ITS Management},
booktitle={Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2025},
pages={45-53},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013229700003941},
isbn={978-989-758-745-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - Spatio-Temporal Traffic Prediction for Efficient ITS Management
SN - 978-989-758-745-0
AU - Nasser A.
AU - Simon V.
PY - 2025
SP - 45
EP - 53
DO - 10.5220/0013229700003941
PB - SciTePress