AI-Based Traffic Management System
Abstract
The AI-Based Traffic Management System is an intelligent, real-time traffic optimization platform designed to reduce congestion and improve intersection efficiency using computer vision and machine learning. By processing live video feeds, the system automatically detects and counts vehicles, classifies traffic density, and dynamically adjusts traffic signal timings to maintain optimal flow. The solution provides a scalable, low-cost alternative to traditional sensor-based infrastructure, enabling smart city traffic automation with improved safety, reduced waiting times, and enhanced overall mobility.
Author Information
Project Details
Description
This AI-driven traffic management system integrates real-time video analytics with adaptive signal control algorithms to deliver a smarter and more efficient urban traffic solution. Using computer vision models—powered by deep learning frameworks—the system continuously monitors intersections and extracts key traffic parameters such as vehicle count, congestion levels, and lane occupancy. The backend processing pipeline analyzes these inputs and computes optimal signal durations for each lane, ensuring that green-light timing dynamically adapts to changing traffic conditions. This approach prevents unnecessary delays, reduces queue lengths, and improves throughput during peak hours. The system includes a dashboard for traffic authorities, allowing them to visualize live traffic data, view congestion analytics, and override signal timings during emergencies. With its modular architecture, the platform supports integration with existing traffic infrastructure and can scale across multiple intersections in a city.
What's Included
90-Day Access
After purchase, you'll have 90 days to download all project files. Access expires automatically after this period.