SmartTraffic-TCMS

Smart Traffic Congestion Management System (TCMS) is an AI-powered system that dynamically adjusts traffic light timings based on real-time vehicle density using YOLOv5 and adaptive algorithms. It reduces congestion, optimizes traffic flow, and improves urban mobility.

🚦 Traffic Congestion Management System (TCMS)

📌 Overview

The Traffic Congestion Management System (TCMS) is an intelligent traffic light management system that dynamically adjusts signal timings based on real-time vehicle density using YOLOv5 and a signal-switching algorithm.

📷 Screenshots

Vehicle Detection Output:

Simulation GUI:

🚀 Features

✅ Real-time Vehicle Detection: Uses YOLOv5 to detect and classify vehicles.
✅ Dynamic Traffic Signal Control: Adjusts signal durations based on traffic density.
✅ Simulation & Visualization: GUI-based simulation using Pygame.
✅ Efficient Traffic Flow Management: Reduces congestion by up to 25%.
✅ Data Logging: Stores detection results and signal timings for analysis.

🛠 Tech Stack

• Python: Main programming language
• YOLOv5: Object detection model for vehicle recognition
• Pygame: GUI-based traffic simulation
• OpenCV: Image processing
• NumPy, Pandas: Data handling and analytics

📁 Repository Structure

TCMS/ 
│── output_images/           # Contains generated output images  
│── test_images/             # Contains test images for processing  
│── FINALGUI.gif             # Animated demonstration of the GUI  
│── FINAL_GUI.ipynb          # Jupyter Notebook for GUI implementation  
│── LICENSE                  # License for open-source usage  
│── README.md                # Project documentation  
│── TCMS_REPORT.pdf          # Technical report for the project  
│── detection_output.png     # Screenshot of sample output from the detection process  
│── gui_interface.png        # Screenshot of the GUI interface  
│── signal_calculation.ipynb # Jupyter Notebook for signal calculations  

🎯 Installation & Setup

How to Run

You can run the project using Jupyter Notebook or directly in the terminal/bash.

1. Clone the Repository

git clone https://github.com/vaishnavipaswan/SmartTraffic-TCMS.git
cd SmartTraffic-TCMS

2. Install Jupyter (if not installed)

Make sure you have Jupyter installed. If not, install it using:

pip install notebook

3. Run Jupyter Notebook

Start Jupyter Notebook with:

jupyter notebook

Then, open FINAL_GUI.ipynb or signal_calculation.ipynb and execute the cells step by step.

4. View Output

• Processed images are saved in output_images/
• Test images are located in test_images/
• The GUI interface preview is available in FINALGUI.gif or gui_interface.png

📊 Results & Analysis

• Wait Time Reduction: Up to 25% improvement in high-density conditions.
• Throughput Increase: 20% more vehicles processed compared to traditional systems.
• Optimized Signal Timing: Adjusts green light durations dynamically for efficient traffic management.

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📌 Future Enhancements

🔹 Multi-intersection Coordination – Expanding the system to manage multiple intersections.
🔹 Integration with IoT Sensors – Incorporating real-time traffic data from IoT devices.
🔹 Real-time Cloud Deployment – Enabling cloud-based traffic control for scalability.
🔹 Emergency Vehicle Prioritization – Adjusting signals dynamically to prioritize emergency vehicles.

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📜 License

This project is licensed under the MIT License.

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👥 Authors

• Vaishnavi Paswan
• Vedika Agrawal
• Pushkar Dubey
• Mustakeem Shaikh

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⭐ Contributing

Feel free to fork this repository, create a branch, and submit pull requests!
For major changes, please open an issue first to discuss your proposal.

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