Smart Traffic Light Management System

Overview

The Smart Traffic Light Management System is designed to optimize traffic flow at intersections by dynamically adjusting signal timings based on real-time traffic conditions. This system aims to reduce congestion, improve travel times, and enhance overall road safety.

Features

• Real-Time Traffic Monitoring: Utilizes sensors/cameras to gather live traffic data.
• Dynamic Signal Control: Adjusts traffic light durations based on current traffic density.
• Emergency Vehicle Detection: Prioritizes emergency vehicles to ensure swift passage.
• Data Analytics: Collects and analyzes traffic data for future infrastructure planning.
• Pygame Simulation: Provides a visual simulation of the traffic light system using the Pygame library.

Installation

1. Clone the Repository:

   git clone https://github.com/dev0052/Smart-Traffic-Light-Management-System.git
   cd Smart-Traffic-Light-Management-System

2. Set Up the Environment:

   • Ensure you have Python 3.x installed.

   • Create and activate a virtual environment:

     python3 -m venv venv
     source venv/bin/activate  # On Windows, use venv\Scripts\activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Configure Settings:

   • Update any configuration files with your specific settings (e.g., camera IPs, sensor configurations).

Usage

1. Start the System:

   python main.py

2. Access the Dashboard:

   • Open your web browser and navigate to http://localhost:8000 to view the traffic management dashboard.

3. Run the Pygame Simulation:

   • To view the visual simulation of the traffic light system, run the following command:

     python simulation.py

   • The simulation will display a graphical representation of the traffic lights and their dynamic behavior.

Project Structure

• main.py: Entry point of the application.
• simulation.py: Pygame-based simulation of the traffic light system.
• config/: Configuration files.
• src/: Source code for various modules.
• docs/: Documentation and related resources.

Contributing

We welcome contributions to enhance the system's capabilities. Please fork the repository and submit pull requests for review.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

We would like to thank all contributors and the open-source community for their invaluable support.