ATTENTION ENHANCED CONVOLUTIONAL NEURAL NETWORKS FOR TRAFFIC ANOMALY DETECTION

Abstract

This paper investigates Attention Enhanced Convolutional Neural Networks (AE-CNNs) as a potential method for identifying traffic issues in scenarios where the highways are highly complex and are in a state of flux. By incorporating attention processes into conventional convolutional neural network topologies, the proposed approach prioritizes the most pertinent spatial and temporal data. This facilitates the identification of issues such as traffic congestion, unusual vehicle behavior, and collisions. By integrating channel and spatial attention modules, the model eliminates superfluous data and enhances feature representation. This renders it superior to conventional CNN-based methodologies. The system is more resilient, accurate, and sends fewer false warnings, as evidenced by experiments with various traffic datasets. The results indicate that AE-CNNs enhance real-time traffic monitoring and smart transportation, thereby enhancing the efficiency and safety of urban transportation.