URBAN TRAFFIC GOVERNANCE USING DU-NET: A DUAL-STREAM DEEP LEARNING FRAMEWORK FOR REAL-TIME ROAD ANOMALY DETECTION AND INTELLIGENT VEHICLE ANALYTICS

The rapid rise of urbanization and the connected mobility demonstrates the serious limitations facing traditional traffic management systems. Solutions previously focused on vehicle flow. Others relied on manual inspection for road maintenance. Both these were not comprehensive. They also caused delays in answering urban transport issues. In this paper, we will introduce DU-Net (Dual-Stream Urban Network), an intelligent deep-learning framework for real-time detection of road anomalies and vehicle analytics in a smart city. The design utilizes high-definition cathode ray tube roadside camera visual sensing and IOT sensor data from embedded infrastructure for multi-task learning to detect potholes and classify vehicles and dynamically estimate traffic density. A convolutional pipeline with two streams can capture spatial dependencies and subsequent temporal dependencies. A probabilistic fusion model views the hypotheses for sensor signals and video signals as being generated from a probabilistic mixture model. It can align the video-based hypotheses with the sensor-based hypotheses to achieve consistent decisions. The system uses a smart technique that gives an appropriate weight to each task in order to enhance robustness over weather and lighting and traffic conditions. Experimental analysis using urban road datasets shows DU-Net to outperform state-of-the-art detectors like YOLOv8 and Faster R-CNN in terms of evaluation metrics, inference speed, and computational cost. Moreover, its prediction analytics component facilitates scheduling of maintenance and traffic congestion prediction. The suggested DU-Net framework provides an extensible foundation for smart transportation systems in the future settings through data-driven, adaptive and climate-resilient urban traffic governance.