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Title	Machine Learning-Based Anomaly Detection In Smart City Traffic: Performance Comparison And Insights
ID_Doc	36035
Authors	Bawaneh M.; Simon V.
Year	2025
Published	International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS - Proceedings
DOI	http://dx.doi.org/10.5220/0013141100003941
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. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
Author Keywords	Anomaly Detection; Intelligent Transportation Systems; Machine Learning; Smart City; Sustainability; Traffic Congestion

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