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Density Based Traffic Management System

 

 

Authors:

Prashant Shukla, Parth Sharma, Ojas Mayur, Kunal Garg, Prof Dr. Narendra

ABSTRACT

Urban traffic congestion has emerged as a critical challenge in modern cities, with static traffic signal systems exacerbating inefficiencies in traffic flow management. Conventional systems rely on fixed timers that do not adapt to real-time traffic density, leading to prolonged idling, increased fuel consumption, and elevated greenhouse gas emissions.

For instance, the INRIX Global Traffic Scorecard 2022 reported that the average

U.S. driver lost 51 hours annually due to congestion, costing the economy over $81 billion in wasted time and fuel. In developing nations like India, where heterogeneous traffic (cars, bikes, rickshaws, buses) dominates, static systems are particularly ineffective, often causing chaotic intersections and safety hazards.

Despite advancements in IoT and machine learning, existing solutions—such as sensor- based networks or deep learning models— remain constrained by high infrastructure costs, computational complexity, or poor scalability in mixed-traffic environments.

This paper aims to bridge these gaps by proposing an adaptive traffic signal control system that dynamically adjusts green signal timers based on real-time traffic density.

This study underscores the potential of density-based approaches to revolutionize traffic management, particularly in congested urban settings, while identifying critical areas for further research and development.

This paper compares Traffic Management's conceptual framework and methodologies, informed by recent advancements in image- processing their application.

Keywords: Adaptive traffic signals, dynamic scheduling, sustainable urban mobility, image-processing

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