Adaptive Traffic Control Systems

International Research Journal on Advanced Engineering Hub (IRJAEH) 2026 Open access

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Authors: A Aravind Pandiyan, P Sathya, A Khidash Muhaimin Shah, J Ramraj, S Lavanya
DOI: 10.47392/irjaeh.2026.0077
Cited by: 0

Abstract

This project presents an AI-driven Adaptive Traffic Control System (ATCS) that optimizes traffic signal timing using real-time video analytics and vehicle density estimation. The system captures live road footage, processes vehicle flow patterns and lane-wise congestion levels, and extracts meaningful traffic indicators such as queue length, vehicle count, and average waiting time. A machine learning model analyzes these features to dynamically assign optimal green-time durations and classify intersections into Low, Moderate, or High congestion states. Implemented using Streamlit and Python, the system generates a detailed PDF report summarizing intersection performance and predicted traffic load.

Fields of Study & Subjects

Traffic control and management Traffic Prediction and Management Techniques Transportation Systems and Logistics

Publication details

Journal / source
International Research Journal on Advanced Engineering Hub (IRJAEH)
Volume
4
Issue
02
Pages
568–574
Publication date
16 February 2026
Language
EN