The Smart Traffic Management System is a production-level AI solution designed to optimize urban traffic flow.
It combines computer vision, reinforcement learning, and real-time analytics to dynamically adjust traffic signal timings, reduce congestion, and enhance road safety.
The system integrates seamlessly with existing CCTV and IoT infrastructure while offering a modern dashboard for traffic authorities.
- 🔍 Vehicle Detection & Counting: Real-time object detection with YOLOv5 + OpenCV.
- 🚦 Adaptive Signal Control: Reinforcement Learning (PPO agent) with collision-free signal phase design.
- 📊 Live Dashboard: React.js dashboard to monitor traffic density, signals, and system performance.
- 🚑 Priority Overrides: Emergency vehicle detection and pedestrian safety integration.
- 🗄 Traffic Data Storage: MongoDB to log signals, vehicle counts, and historical patterns.
- 🌆 Scalable Design: Built to extend from single intersections to multi-junction networks.
- ⚡ Reduced Congestion: Cuts average commute time by ~10% in simulation.
- 🛡 Safety-First Design: Collision-free phase scheduling ensures safe intersections.
- 📡 Real-Time Monitoring: Centralized dashboard for authorities to track live traffic conditions.
- 🔗 Easy Integration: Works with existing traffic cameras and IoT devices.
- 📈 Data-Driven: Historical insights for better urban planning.
- 🖥 High Processing Power: Real-time computer vision requires GPU/edge acceleration.
- 🔧 Training Effort: RL agent needs substantial training data for complex intersections.
- 📶 Infrastructure Dependent: Performance depends on CCTV/IoT network reliability.
| Layer | Technology |
|---|---|
| Backend | FastAPI (Python) |
| Database | MongoDB |
| Computer Vision | YOLOv8, OpenCV, DeepSORT |
| Reinforcement Learning | Stable-Baselines3 (PPO), SUMO |
| Frontend | React.js + TailwindCSS |
| API | REST (JSON) |
Built with ❤️ to make cities smarter and safer 🌆