Fast transportation systems and rapid transit play pivotal roles in the economic development of any nation. However, mismanagement and the resulting traffic congestion can lead to prolonged waiting times, increased fuel consumption, and financial losses. Although numerous traffic management techniques exist to address congestion, none is inherently flawless, given the constantly changing real-time situations. The primary cause of today`s traffic problems often lies in the shortcomings of existing traffic management systems. These systems often lack a focus on real-time traffic scenarios, resulting in inefficiencies. Our initiative seeks to bridge this gap by introducing a self adjusting traffic management strategy capable of seamlessly adapting to the ever-changing circumstances on the road. Traffic congestion and road safety are persistent challenges in urban areas, necessitating the development of robust Traffic Management Systems (TMS). This abstract provides an overview of a comprehensive TMS designed to address these challenges and improve overall urban mobility.

Image Processing, Signal Processing, Sensor and Measurement Techniques, YOLO model, Radio Frequency Identification, Convolutional Neural Networks, Blob Detection

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