Vaibhav Kunjir | International Journal of Transportation Engineering and Traffic System | Vol 11, Issue 01 | pp. 24-31 | ISSN: 2456-2343
Abstract
Abstract
The rapid increase in the number of vehicles on roads has created significant challenges for
conventional traffic management systems, which struggle to efficiently regulate traffic flow under
varying conditions. Traditional traffic signals operate on fixed timing cycles, failing to adapt to
fluctuating vehicle densities across different lanes. This inefficiency results in unnecessary delays,
increased congestion, longer travel times, and excessive fuel consumption. Additionally, fixed-timing
signals do not account for dynamic traffic patterns, leading to bottlenecks and inefficient road
utilization. To address these challenges, we propose an intelligent traffic signal system equipped with
dynamic signal-switching capabilities. Unlike conventional systems, our approach continuously
monitors real-time traffic conditions and dynamically adjusts signal durations based on vehicle density.
This system employs advanced image processing techniques to detect, analyze, and count the number
of vehicles at intersections. By leveraging real-time data, the system optimizes signal transitions to
prioritize high-traffic lanes, ensuring a smoother and more efficient traffic flow. During peak hours,
the system extends green-light durations for congested lanes while minimizing wait times for less
crowded routes. Additionally, the system can detect emergency vehicles and adjust signals accordingly
to facilitate their movement. By implementing this adaptive traffic control mechanism, we aim to
significantly reduce congestion, improve road network efficiency, and enhance overall urban mobility.
The proposed system can contribute to lower fuel consumption, reduced emissions, and a more
sustainable transportation infrastructure. Moreover, the integration of real-time traffic monitoring can
assist city planners in making data-driven decisions for optimizing road layouts and infrastructure
development. This intelligent traffic management solution has the potential to revolutionize traditional
traffic control methods, leading to safer, more efficient, and environmentally friendly urban
transportation systems.
Keywords: Traffic congestion, vehicle detection, image processing, machine learning, intelligent
traffic control
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