Real-Time Traffic Analysis

Overview

This project aims to optimize traffic management using machine learning techniques. It incorporates real-time vehicle detection, counting, emergency vehicle identification, and adaptive traffic light control. By leveraging deep learning models and IoT-enabled sensors, this system addresses challenges in urban traffic congestion and emergency response efficiency.

Key Features

• Real-time Traffic Analysis: Processes live video streams to detect and count vehicles dynamically.
• Emergency Vehicle Detection: Identifies emergency vehicles using advanced algorithms for priority routing.
• Adaptive Traffic Signal Control: Optimizes traffic light timings based on lane-specific congestion levels.
• Vehicle Classification: Distinguishes between emergency and non-emergency vehicles using deep learning models.
• Scalability: Designed to handle diverse urban environments and varying traffic patterns.

Implementation

The project integrates the following components:

1. Frame Extraction: Extracts frames from video streams for analysis.
2. Vehicle Detection and Counting: Uses YOLOv3 for accurate and efficient vehicle detection.
3. Traffic Congestion Analysis: Analyzes lane-specific congestion to optimize traffic flow.
4. Emergency Vehicle Identification: Detects unique patterns like sirens and flashing lights.
5. Adaptive Signal Management: Dynamically adjusts traffic light signals to prioritize emergency routes and reduce congestion.

Technologies Used

• Programming Languages: Python
• Frameworks: TensorFlow, OpenCV
• Deep Learning Models: YOLOv3, CNNs
• Visualization Tools: Matplotlib
• Hardware: IoT-enabled sensors and cameras

Results

• Improved traffic flow efficiency through dynamic signal control.
• Enhanced emergency response times by prioritizing emergency vehicles.
• Achieved accurate vehicle detection and classification using YOLOv3 and CNNs.

Future Scope

• Incorporate predictive analytics to forecast traffic congestion.
• Extend compatibility with smart city infrastructures.
• Deploy more advanced deep learning models for higher accuracy.

How to Use

1. Clone the repository:

   git clone https://github.com/your-username/real-time-traffic-analysis.git

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   python main.py

Contributors

• Praneesh Sharma
  Developed the vehicle counting system, focusing on analyzing video input to determine traffic volume in each lane and implementing logic for adaptive traffic signal control.
• Nayeer Naushed
  Designed and implemented the vehicle classification system to distinguish between emergency and non-emergency vehicles, aiding in priority-based traffic management.
• Shravan Serel
  Conducted research by reviewing academic papers and handled the project's documentation to ensure comprehensive and accurate reporting.