Waygen: AI-Powered Smart Traffic Management System

AI-powered roads, seamless journeys – Built with MapMyIndia

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Click the button above to visit the live version of the WayGen project hosted on Vercel.

🌟 Overview

Waygen is an AI-driven Smart Traffic Management System designed to revolutionize urban mobility by integrating computer vision, IoT, and geospatial intelligence. Our solution addresses the critical challenges of urban traffic congestion through real-time monitoring, analysis, and optimization.

🚦 Problem Statement

Urban traffic congestion is a major challenge, leading to:

• Delays and wasted time
• Increased fuel consumption and pollution
• Higher accident rates
• Emergency vehicle delays
• Inefficient traffic flow

Conventional traffic lights operate on fixed timers which do not adapt to real-time traffic conditions. This causes:

• Wasted green lights on empty lanes
• Vehicle pile-ups in congested directions
• Long queues and increased delays
• Emergency vehicle struggles in critical situations
• Outdated systems compared to modern smart adaptive solutions

💡 Our Solution

WayGen revolutionizes traffic management through:

• AI-driven traffic analysis - YOLO-based vehicle detection continuously monitors traffic flow
• Dynamic signal adjustment - Traffic lights adapt in real-time based on congestion patterns
• IoT integration - Raspberry Pi-powered signal controllers ensure seamless optimization
• Geospatial intelligence - MapMyIndia integration provides real-time navigation and alternative routes
• Emergency vehicle prioritization - Automatic detection and signal adjustment for emergency vehicles
• Congestion prediction - AI models analyze patterns to prevent bottlenecks before they occur

🔑 Core Features

• ✅ AI-based Real-time Traffic Light Control

  • Monitors traffic density and adjusts signal timings dynamically
  • Reduces wait times and improves traffic flow efficiency

• ✅ Emergency Vehicle Detection & Prioritization

  • Detects sirens and visual identification of emergency vehicles
  • Automatically adjusts signals to create green corridors

• ✅ Live Traffic Congestion Heatmap

  • Visualizes congestion levels across the monitored area
  • Helps users identify and avoid traffic hotspots

• ✅ Alternate Route Recommendations

  • Suggests optimal alternative routes during heavy traffic
  • Integrates with MapMyIndia for accurate navigation

• ✅ Traffic Flow Predictions

  • Analyzes historical data to predict future congestion patterns
  • Enables proactive traffic management strategies

🔧 Technical Architecture

Components Overview

1. Data Collection Layer

   • CCTV cameras & drones capture live video feeds
   • Microphone sensors detect emergency vehicle sirens
   • IoT devices (Raspberry Pi & Arduino) process sensor inputs

2. AI Processing Layer

   • YOLOv8 for vehicle detection and classification
   • DeepSORT for vehicle tracking
   • OpenCV for image processing
   • Flask for API management

3. Backend Processing

   • MongoDB for data storage
   • WebSocket for real-time communication
   • Traffic analysis algorithms

4. Frontend Dashboard

   • React-based responsive interface
   • Real-time traffic analytics visualization
   • MapMyIndia integration for geospatial display

5. Signal Control System

   • Priority-based signal switching for emergency vehicles
   • Adaptive traffic light control based on congestion analysis

💻 Technology Stack

Frontend

• React - UI library for building the dashboard
• MapMyIndia SDK - For maps integration and geospatial visualization
• WebSocket - For real-time communication with the backend

Backend

• Flask - Python web framework for the API
• MongoDB - Database for storing traffic data and patterns
• WebSocket - For real-time data transmission

AI & Computer Vision

• YOLOv8 - For real-time object detection
• DeepSORT - For object tracking
• OpenCV - For image processing
• Python - Primary programming language

Hardware

• Raspberry Pi - For edge computing and signal control
• Arduino - For sensor integration
• CCTV Cameras - For traffic monitoring

🚀 Getting Started

Prerequisites

• Python 3.8+
• Node.js and npm
• MongoDB
• MapMyIndia API key

Installation

1. Clone the repository

   git clone https://github.com/ArshTiwari2004/Waygen.git
   cd Waygen

2. Setup Frontend

   cd traffic-monitoring/frontend
   npm install

3. Create a .env file in the root of the frontend directory with:

   VITE_MAPPLS_SDK_KEY=your_mapmy_india_sdk_key

4. Start the development server:

   npm run dev
   

5. Setup Backend - Create and activate a virtual environment:

   python -m venv venv
   venv\Scripts\activate  # Windows
   source venv/bin/activate  # Linux/Mac
   

6. Install the required packages:

   pip install -r requirements.txt
   

7. Start the Flask server:

   python backend/flask_api.py
   

Project Structure

Waygen/
├── .git/
├── .ipynb_checkpoints/
├── .vscode/
├── traffic-monitoring/
│ ├── backend/
│ ├── data/
│ ├── frontend/
│ ├── notebooks/
│ ├── simulation/
│ └── yolov8n.pt
└── README.md

🔮 Future Enhancements

• AI-powered license plate recognition
• Reinforcement learning can optimize signal patterns dynamically based on historical and real-time traffic flow
• Detect accidents or vehicle breakdowns.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

• Fork the repository
• Create your feature branch (git checkout -b feature/amazing-feature)
• Commit your changes (git commit -m 'Add some amazing feature')
• Push to the branch (git push origin feature/amazing-feature)
• Open a Pull Request