Year: 2025
Type: article
Abstract: Urban traffic congestion is increasing at an alarming rate due to fixed-time traffic signals that fail to adapt to real-time variations in vehicle density. This research proposes an AI-based Smart Traffic Control System that uses YOLO object detection and weighted density computation to dynamically adjust green-signal duration for each lane. The system continuously captures live video feeds from cameras installed at intersections, detects and classifies vehicles, calculates lane-wise density scores and allocates signal time proportionally. Experimental analysis demonstrates high detection accuracy (92.5%), reduced average waiting time, improved vehicle throughput and efficient emergency vehicle prioritisation. The proposed architecture is scalable, cost-effective and suitable for deployment in heterogeneous traffic conditions in modern smart cities.
Authors: Ananya A G, Bhoomika S N, Manju U, Vinay Gowda K P, Abdul Rahaman
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4417216699
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