International Journal of Computational and Electronics Aspects in Engineering
Volume 7 · Issue 1 · February 2026 · pp. 73-81
Research Article · Peer Reviewed
Received: December 20, 2025 · Accepted: February 20, 2026 · Published: 04 March 2026
DOI: 10.26706/ijceae.7.1.20260108
Open Access · CC BY 4.0

A Deep Learning–Based Framework for Intelligent Traffic Management

Farah Amer Abdulaziz*
Department of Computer Science, College of Computer Science and Mathematics, Tikrit University, Tikrit, Iraq.

*Corresponding author: farar.amer33322@tu.edu.iq
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Abstract

This paper explores the potential of deep learning techniques in improving the classification of road signals in transport networks. Traditional traffic light monitoring methods rely heavily on manual observation, which can be slow and error-prone, leading to delays in responding to traffic changes. The proposed system is based on the YOLO v7 model to detect and classify traffic lights, enhancing traffic flow, efficiency, and safety while minimizing congestion. A specialized dataset was used for training and evaluation. The system captures images using a single camera and applies deep learning techniques to develop a smart model capable of accurately classifying traffic lights in real time. Experimental results indicate high precision and recall rates, demonstrating the effectiveness of the approach. This study contributes to the advancement of real-time traffic control systems by providing a robust solution for automatic traffic light classification, supporting smart transportation systems and future traffic management initiatives.

Keywords

AI Deep Learning YOLO Traffic Light Artificial Intelligence Object Detection Intelligent Traffic Management Smart Transportation Real-Time Classification

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