PREDICTIVE ANALYTICS FOR ACCIDENT DETECTION IN INTELLIGENT TRAFFIC SYSTEMS: A SMART CITY PERSPECTIVE

Abstract

The integration of intelligent traffic systems in smart cities enhances road safety and traffic efficiency by utilizing predictive analytic for road accident detection. Recent advancements have arranged a multidisciplinary approach integrate machine learning, Internet of Things (IOT), and deep learning methodologies reorganized traffic management. This approach includes the development of a 3D Convolution Neural Network (CNN) based system for automatic accident detection using traffic video analysis. This system optimizes detection during varied weather and lighting conditions, significantly improving road safety. Leveraging real-time data from wireless sensor networks and predictive models can accurately forecast traffic flow and road occupancy rates. This predictive capability is essential for dynamically adjusting traffic control measures, reducing congestion and facilitating a smoother traffic flow. The combination of these technologies marks a significant evolution in intelligent transportation systems, effectively primarily accidents and optimizing urban mobility infrastructure.