Traffic Management System using Graph Neural Networks and Reinforcement Learning

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Overview

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This project presents a novel approach to traffic management using machine learning, combining computer vision, graph neural networks, and reinforcement learning to optimize traffic signal timings and reduce traffic congestion.

System Components

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1. Traffic Monitoring System

• Uses computer vision to analyze live camera feeds and detect traffic congestion
• Utilizes OpenCV to process video feeds and detect the number of vehicles in each lane

2. Graph Neural Network

• Models the traffic network and predicts traffic congestion using a graph neural network
• Implemented using PyTorch Geometric library

3. Reinforcement Learning Agent

• Optimizes traffic signal timings using reinforcement learning
• Utilizes the FRAP algorithm to learn the optimal traffic signal timings

How it Works

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Step 1: Traffic Monitoring

The traffic monitoring system uses computer vision to analyze live camera feeds and detect traffic congestion.

Step 2: Graph Neural Network

The graph neural network models the traffic network and predicts traffic congestion.

Step 3: Reinforcement Learning

The reinforcement learning agent optimizes traffic signal timings using the FRAP algorithm.

System Architecture

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                              +---------------+
                              |  Camera Feeds  |
                              +---------------+
                                     |
                                     |
                                     v
                              +---------------+
                              |  Traffic Monitoring  |
                              |  (Computer Vision)    |
                              +---------------+
                                     |
                                     |
                                     v
                              +---------------+
                              |  Graph Neural Network  |
                              |  (Traffic Network Modeling) |
                              +---------------+
                                     |
                                     |
                                     v
                              +---------------+
                              |  Reinforcement Learning  |
                              |  (Traffic Signal Optimization) |
                              +---------------+

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Getting Started

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Prerequisites

• Python 3.7+
• OpenCV 4.5+
• PyTorch 1.9+
• PyTorch Geometric 2.0+
• FRAP library

Installation

1. Clone the repository: git clone https://github.com/your-username/traffic-management-system.git
2. Install the required libraries: pip install -r requirements.txt
3. Run the system: python main.py

Results

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The system has been tested on a simulated traffic network and has shown promising results in reducing traffic congestion.

Future Work

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• Integrate the system with real-world traffic data
• Improve the graph neural network architecture for better traffic prediction
• Experiment with other reinforcement learning algorithms for traffic signal optimization

License

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

Acknowledgments

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• This project was inspired by the work of Ritesh Godse on traffic signal control using reinforcement learning.
• The FRAP algorithm was implemented based on the paper "FRAP: A Flexible Reinforcement Learning Algorithm for Traffic Signal Control" by Ritesh Godse.

Contributors

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• Ritesh Godse: Developed the traffic management system using graph neural networks and reinforcement learning.