Machine Learning-Based Anomaly Detection in Smart City Traffic: Performance Comparison and Insights

Mohammad Bawaneh, Vilmos Simon

2025

Abstract

In recent years, urban roads have suffered from substantial traffic congestion due to the rapidly increasing number of road users and vehicles. Some traffic congestion patterns on specific roadways, such as the recurring congestion during morning and evening rush hours, can be foreseen. However, unexpected events, such as incidents, may also cause traffic congestion. Monitoring traffic status poses vital importance for city traffic operators. They can leverage the monitoring system for resource allocation, traffic lights adjusting, and adapting the public transport schedules to alleviate traffic congestion. Machine learning-based methods for anomaly detection are valuable tools for monitoring traffic status and promptly detecting congestion on city roads. In this paper, we comprehensively study the performance of the common machine learning methods for anomaly detection in the traffic congestion detection use case. In addition, we provide methods usage insights based on the study findings by examining the accuracy, detection speed, and computation overhead of the methods to guide the researchers and city operators toward a suitable method based on their needs.

Paper Citation

in Harvard Style

Bawaneh M. and Simon V. (2025). Machine Learning-Based Anomaly Detection in Smart City Traffic: Performance Comparison and Insights. 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 309-318. DOI: 10.5220/0013141100003941

in Bibtex Style

@conference{vehits25,
author={Mohammad Bawaneh and Vilmos Simon},
title={Machine Learning-Based Anomaly Detection in Smart City Traffic: Performance Comparison and Insights},
booktitle={Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2025},
pages={309-318},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013141100003941},
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 - Machine Learning-Based Anomaly Detection in Smart City Traffic: Performance Comparison and Insights
SN - 978-989-758-745-0
AU - Bawaneh M.
AU - Simon V.
PY - 2025
SP - 309
EP - 318
DO - 10.5220/0013141100003941
PB - SciTePress