Anomaly Detection in Traffic Systems
C. R. Jothy, J. E. Judith, Jose Anand · 2025
There is an effective need to manage the existing traffic systems due to the rapid increase in production and usage of vehicles. Traffic congestion, crashes and delays are some of the challenges being faced today. Neural networks have emerged as a powerful solution to tackle the dynamic and complex nature of traffic systems. This chapter, “Anomaly Detection in Traffic Systems,” discusses the application of neural networks in identifying traffic anomalies by highlighting its importance in enhancing safety, efficiency, and overall traffic management. As urban areas continue to grow the prevalence of traffic anomalies such as congestion, accidents, and unexpected patterns poses significant challenges for transportation authorities. The chapter emphasizes the role of machine learning (ML) and deep learning (DL) techniques and highly focuses on the role of neural networks, in identifying complex patterns within traffic data. The chapter also explores how neural networks deal with the Traffic Management System (TMS) to make it intelligent.