Smart Transportation Revolution: Adaptive Monitoring with Squeeze and Excitation Networks

In the rapidly advancing field of smart cities, effective traffic management is essential for improving safety, alleviating congestion, and minimizing environmental damage. This paper rigorously examines the function of deep learning in adaptive traffic monitoring and presents an innovative method utilizing Squeeze and Excitation Networks (SENets). SENets enhance traffic monitoring systems by dynamically recalibrating feature maps, allowing for the prioritization of important features and hence boosting detection accuracy in intricate urban settings. The suggested system combines real-time data from IoT sensors, security cameras, and vehicular networks, offering a comprehensive framework for traffic monitoring and accident prevention. Experimental findings indicate that the SENets-based model attains a 94.5% accuracy in identifying traffic abnormalities and enhances signal control optimization by 30% relative to traditional methods. These findings highlight the capacity of SENets to transform urban traffic management, providing a scalable and economical solution for intelligent transportation systems.