Smart Traffic Signal Control System

An intelligent traffic signal control system simulation that dynamically optimizes traffic light timing based on real-time vehicle detection using computer vision and adaptive algorithms.

Features

• Real-time Vehicle Detection: YOLOv7-based vehicle detection and classification
• Adaptive Signal Timing: Dynamic traffic light control based on vehicle density
• Multi-directional Traffic Flow: Simulates 4-way intersection (Up, Down, Left, Right)
• Vehicle Type Classification: Detects and differentiates between cars, bikes, buses, trucks, and rickshaws
• Visual Simulation: Interactive Pygame-based traffic simulation with realistic graphics
• Performance Analytics: Tracks wait times, queue lengths, and signal efficiency

Project Structure

smart-traffic-sim/
├── simulation.py              # Main simulation engine
├── vehicle_detection.py       # YOLO-based vehicle detection module
├── signal_time.py            # Adaptive signal timing logic
├── yolov7.cfg                # YOLO model configuration
├── coco.names                # Class labels for object detection
├── Simulation Video.mp4      # Demo video
│
├── images/
│   ├── intersection.jpg      # Background intersection image
│   ├── mod_int.png          # Modified intersection layout
│   │
│   ├── signals/             # Traffic light images
│   │   ├── red.png
│   │   ├── yellow.png
│   │   └── green.png
│   │
│   ├── up/                  # Vehicles moving up
│   ├── down/                # Vehicles moving down
│   ├── left/                # Vehicles moving left
│   └── right/               # Vehicles moving right
│       ├── bike.png
│       ├── bus.png
│       ├── car.png
│       ├── rickshaw.png
│       └── truck.png
│
├── output_images/           # Processed detection outputs
└── test_images/             # Sample images for testing

Installation

Prerequisites

• Python 3.7 or higher
• pip package manager
• Webcam (optional, for real-time detection)

Required Libraries

pip install pygame
pip install opencv-python
pip install numpy
pip install pillow

For YOLO Detection (if using deep learning model)

pip install torch torchvision
# OR
pip install tensorflow

Setup

1. Clone the repository:

git clone https://github.com/yourusername/smart-traffic-sim.git
cd smart-traffic-sim

2. Install dependencies:

pip install -r requirements.txt

3. Download YOLO weights (if not included):

# Download YOLOv7 weights
wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt

Usage

Run the Simulation

python simulation.py

Run Vehicle Detection Only

python vehicle_detection.py --image test_images/sample.jpg

Run with Custom Parameters

python simulation.py --spawn-rate 5 --simulation-time 300

How It Works

1. Vehicle Detection

• Uses YOLOv7 model trained on COCO dataset
• Detects and classifies vehicles in each direction
• Counts vehicles at each traffic signal

2. Adaptive Signal Timing

• Calculates vehicle density for each lane
• Allocates green signal duration based on:
  • Number of vehicles waiting
  • Vehicle types (different weights for cars, trucks, buses)
  • Minimum and maximum time constraints

3. Traffic Simulation

• Pygame renders the intersection and vehicles
• Vehicles spawn at configurable rates
• Traffic lights change based on detection algorithm
• Visual feedback shows queue lengths and signal states

Algorithm

# Simplified signal timing logic
def calculate_signal_time(vehicle_count, vehicle_types):
    base_time = 10  # seconds
    
    # Weight different vehicle types
    weights = {
        'car': 1,
        'bike': 0.5,
        'bus': 2,
        'truck': 2,
        'rickshaw': 0.7
    }
    
    weighted_count = sum(weights[vtype] for vtype in vehicle_types)
    signal_time = base_time + (weighted_count * 2)
    
    # Apply constraints
    return min(max(signal_time, MIN_TIME), MAX_TIME)

Configuration

Edit parameters in simulation.py:

# Simulation Settings
DEFAULT_SPAWN_RATE = 3        # Vehicles per direction per cycle
SIMULATION_DURATION = 600     # Seconds
SCREEN_WIDTH = 1400
SCREEN_HEIGHT = 800

# Signal Timing
MIN_GREEN_TIME = 10           # Minimum green signal (seconds)
MAX_GREEN_TIME = 60           # Maximum green signal (seconds)
YELLOW_TIME = 3
ALL_RED_TIME = 2

# Vehicle Settings
VEHICLE_SPEEDS = {
    'car': 2.0,
    'bike': 1.5,
    'bus': 1.8,
    'truck': 1.5,
    'rickshaw': 1.3
}

Controls

• SPACE: Pause/Resume simulation
• R: Reset simulation
• +/-: Increase/Decrease vehicle spawn rate
• S: Save screenshot
• Q/ESC: Quit simulation

Performance Metrics

The system tracks and displays:

• Average Wait Time: Mean time vehicles wait at signals
• Queue Length: Number of vehicles waiting per direction
• Throughput: Vehicles passing through per minute
• Signal Efficiency: Ratio of green time utilization
• Congestion Index: Overall traffic congestion level

Vehicle Types & Priorities

Vehicle Type	Speed	Priority Weight	Image
Bike	Fast	0.5	🏍️
Car	Medium	1.0	🚗
Rickshaw	Slow	0.7	🛺
Bus	Medium	2.0	🚌
Truck	Slow	2.0	🚛

Detection Model

• Model: YOLOv7
• Dataset: COCO (80 classes, filtered for vehicles)
• Accuracy: ~95% for vehicle detection
• Processing: Real-time at 30 FPS

Detected Classes

car, motorcycle, bus, truck, bicycle

Customization

Add New Vehicle Types

1. Add vehicle image to images/{direction}/ folders
2. Update vehicle spawn logic in simulation.py:

VEHICLE_TYPES = ['car', 'bike', 'bus', 'truck', 'rickshaw', 'your_vehicle']

Modify Intersection Layout

Replace images/intersection.jpg with your custom intersection image.

Adjust Detection Sensitivity

Edit vehicle_detection.py:

CONFIDENCE_THRESHOLD = 0.5    # Detection confidence
NMS_THRESHOLD = 0.4           # Non-max suppression

Examples

Example Output

=== Traffic Simulation Report ===
Simulation Time: 300 seconds
Total Vehicles Processed: 487

Direction Statistics:
- North: 125 vehicles, Avg Wait: 23.4s
- South: 118 vehicles, Avg Wait: 21.7s
- East:  122 vehicles, Avg Wait: 24.1s
- West:  122 vehicles, Avg Wait: 22.8s

Signal Performance:
- Green Time Utilization: 87.3%
- Average Cycle Time: 78.5s
- Congestion Index: Medium

Troubleshooting

Issue: Pygame window not opening

# Install SDL dependencies (Linux)
sudo apt-get install python3-pygame

Issue: YOLO model not loading

# Verify model weights file exists
ls -lh yolov7.pt

Issue: Low FPS

• Reduce screen resolution
• Disable real-time detection
• Use lighter YOLO model (YOLOv7-tiny)

Future Enhancements

• Emergency vehicle priority system
• Pedestrian crossing integration
• Multi-intersection coordination
• Machine learning-based prediction
• Real-world camera feed integration
• Mobile app for traffic monitoring
• Historical data analysis
• Weather condition adaptation

Requirements.txt

pygame>=2.0.0
opencv-python>=4.5.0
numpy>=1.19.0
Pillow>=8.0.0
torch>=1.9.0
torchvision>=0.10.0

Contributing

Contributions are welcome! Please:

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

License

This project is licensed under the MIT License - see LICENSE for details.

Acknowledgments

• YOLOv7 by WongKinYiu
• Pygame community
• Traffic engineering principles from ITE (Institute of Transportation Engineers)

Authors

Your Name - @yourgithub

Citation

If you use this project in your research, please cite:

@software{smart_traffic_sim,
  author = {Your Name},
  title = {Smart Traffic Signal Control System},
  year = {2024},
  url = {https://github.com/yourusername/smart-traffic-sim}
}

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⚠️ Note: This is a simulation project for educational purposes. Real-world deployment requires traffic engineering expertise, regulatory approval, and extensive safety testing.