SMC-Niyantran 🚦

AI-powered Smart City Traffic Platform for Solapur Municipal Corporation

1. Project Title and Description

SMC-Niyantran (Smart Traffic) is a decentralized, AI-driven traffic intelligence platform designed for the Solapur Municipal Corporation (SMC). The system utilizes existing CCTV infrastructure and edge computing (Raspberry Pi 4) to eliminate the inefficiencies of fixed 60-second traffic signals, saving commute time, fuel, and reducing carbon emissions. It features automatic encroachment detection, dynamic RL-based signal optimization, auto E-Challan generation, and a specialized one-click "Yatra Mode" for managing massive pilgrim crowds during the Siddheshwar Yatra.

2. Features

• Dynamic RL Optimizer: Replaces fixed timers with adaptive signal control based on real-time vehicle density (reducing wait times by ~68.7%).
• Virtual Geofencing & Encroachment Detection: Automatically detects vehicles parked in unauthorized zones using YOLOv8 bounding boxes.
• Auto E-Challan Generation: Generates real-time infraction logs with unique challan IDs in under 40ms with zero manual enforcement.
• Yatra Mode (Emergency Override): One-click fail-safe to prioritize pilgrim and emergency vehicle flow during major festivals.
• Eco-Niyantran (Carbon Calculator): Tracks cumulative CO2 emissions saved by reducing vehicle idle times (up to 4.2 tonnes CO2/day citywide).
• AI Live Assistant: Integrated Gemini 1.5 Flash for context-grounded queries and parking/vehicle analysis.

3. Tech Stack

• AI / Computer Vision: YOLOv8n, OpenCV 4.9, NumPy, PyTorch 2.0 (CPU only)
• Backend: Python, FastAPI, Uvicorn
• Frontend: React 18, Vite, TypeScript, Tailwind CSS, shadcn/ui, Recharts
• LLM / Generative AI: Google Gemini 1.5 Flash (Context-grounded)
• Hardware Profile: Raspberry Pi 4 (Edge device), existing RTSP/CCTV IP cameras

4. Installation Instructions

Prerequisites

• Python 3.9+
• Node.js 18+ & npm
• Basic C/C++ build tools (for OpenCV dependencies)

Backend Setup

# 1. Navigate to the backend directory
cd backend

# 2. Create and activate a virtual environment
python -m venv venv
source venv/bin/activate      # On Windows: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Provide a video feed
# Place your CCTV feed as 'traffic_feed.mp4' in the backend/ directory
# (If missing, the system will automatically run a synthetic demo feed)

Frontend Setup

# 1. Navigate to the frontend directory
cd frontend2

# 2. Install dependencies
npm install

5. Usage

Running the Backend

cd backend
source venv/bin/activate
uvicorn main:app --host 0.0.0.0 --port 8000 --reload

The API will be live at http://localhost:8000. Auto-generated Swagger docs available at http://localhost:8000/docs.

Running the Frontend

cd frontend2
npm run dev

The React Dashboard will be accessible at http://localhost:8080 (or the port specified by Vite, typically 5173).

6. Project Structure

Smart Traffic/
├── backend/                  # FastAPI Backend & AI Engine
│   ├── main.py               # Core application and video processing threads
│   ├── requirements.txt      # Python dependencies
│   ├── traffic_feed.mp4      # Live CCTV or demo footage
│   └── yolov8n.pt            # Pre-trained YOLOv8 Nano weights
│
├── frontend2/                # React Vite Application
│   ├── src/                  # React components, pages, and hooks
│   ├── public/               # Static assets
│   ├── package.json          # Node dependencies
│   ├── tailwind.config.ts    # Tailwind UI configurations
│   └── components.json       # shadcn/ui component definitions
│
└── README.md                 # Project Documentation

7. API Documentation

The FastAPI backend serves several key endpoints for the React dashboard:

Method	Endpoint	Description
GET	/api/video-feed	Streams live processed MJPEG video frames with YOLO bounding boxes.
GET	/api/traffic-data	Polled for real-time metrics (vehicle counts, active alerts, green times).
GET	/api/alert-log	Retrieves history of encroachment violations and auto-generated E-Challans.
GET	/api/carbon-log	Fetches a 30-day rolling array of CO2 metrics and RL learning efficiency.
GET	/api/system-stats	Provides system FPS, frame counts, and hardware uptime metrics.
POST	/api/settings	Hot-reloads configuration (e.g., Yatra Mode, zone coordinates, confidence).

Sample response for /api/traffic-data:

{
  "vehicle_count": 12,
  "encroachment_alert": true,
  "dynamic_green_time": 36,
  "carbon_saved_kg": 4.2,
  "yatra_mode": false,
  "fps": 24.5,
  "backend_status": "live"
}

8. Screenshots

Dashboard

Figure 1: Main React Dashboard showing live CCTV feed, YOLOv8 detections, and dynamic RL timers.

Alert Logs

Figure 2: Real-time Encroachment Detection and Auto E-Challan Drafting.

Analytics

Figure 3: Eco-Niyantran 30-Day Carbon Emission Reduction Analytics.

Settings

Figure 4: Hot-reloadable Settings Panel and Yatra Mode activation.

(Note: Create a screenshots folder in the repository root and place your actual screenshots using these filenames.)

9. Configuration

• Backend Configuration: The application hot-reloads settings directly via the /api/settings endpoint. Initial default parameters like BASELINE_GREEN, CARBON_PER_VEH_SEC, and ZONE coordinates are hardcoded at the top of backend/main.py but can be dynamically overridden by the frontend.
• Frontend Configuration: The frontend communicates directly with http://localhost:8000. Ensure proxy settings in Vite or .env files are updated if you deploy the backend on a remote server.

10. Contributing Guidelines

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

11. License

This project is open-source and distributed under the MIT License.

12. Author / Credits

Team Name: VYOMAN