Smart Traffic Optimization System
Our project is an AI-driven smart traffic management system that dynamically controls traffic lights based on real-time vehicle detection. Using computer vision and adaptive timing, the system minimizes idle time at intersections, reducing fuel waste and improving traffic flow efficiency.
The platform features a dual-camera simulation powered by OpenCV and a custom light management module connected to physical LEDs. Each lane’s signal is controlled using dynamic logic that responds to traffic density, preventing unnecessary delays.
Beyond efficiency, the system tracks fuel and money saved in real time, visualizing the environmental and economic benefits of smarter transportation. The result is a self-regulating, eco-friendly traffic controller that demonstrates how AI and IoT can work together to make cities cleaner and smarter.
Key Features:
Real-time car detection with OpenCV
Adaptive light switching based on lane congestion
Live dashboard showing traffic state, fuel saved, and money saved
Physical LED integration for realistic signal output
Modular codebase for integration with real-world sensors or cameras
Tech Stack: Python · OpenCV · Arduino (via serial communication) · Custom hardware interface
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