Unveiling Traffic Patterns for Effective Traffic Management System

Traffic congestion is a persistent global challenge that has detrimental effects on productivity, economies, and the environment. This article explores image detection techniques, specifically focusing on the Gaussian blur method, for accurate vehicle detection and analysis. The application of image detection techniques holds potential for identifying traffic patterns, analyzing traffic density, and monitoring traffic volume, thereby providing valuable insights for urban planners and transportation authorities. This research article proposes enhanced vehicle detection algorithm using Gaussian blur method for noise reduction and object clarity, leading to enhanced accuracy for diverse dataset of real-world videos from various traffic scenarios. The findings have direct implications for addressing traffic congestion, as they contribute to the development of efficient traffic management strategies and the optimization of traffic flow. This research highlights the promising capabilities of image detection technologies in addressing traffic- related challenges and enhancing overall traffic management systems in urban environments.