https://github.com/godseritesh/intelligent_traffic_manager_agent

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.
https://github.com/godseritesh/intelligent_traffic_manager_agent

automobile-industry traffic-analysis traffic-management

Last synced: 7 months ago
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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.

• Host: GitHub
• URL: https://github.com/godseritesh/intelligent_traffic_manager_agent
• Owner: godseritesh
• Created: 2024-09-23T05:03:14.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-09-23T05:37:54.000Z (about 1 year ago)
• Last Synced: 2025-01-26T20:16:48.287Z (8 months ago)
• Topics: automobile-industry, traffic-analysis, traffic-management
• Language: Python
• Homepage:
• Size: 709 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Traffic Management System using Graph Neural Networks and Reinforcement Learning

===========================================================

## Overview

-----------

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

--------------------

### 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

----------------

### Step 1: Traffic Monitoring

![Traffic Monitoring](traffic_monitoring.jpg)

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

### Step 2: Graph Neural Network

![Graph Neural Network](graph_neural_network.jpg)

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

### Step 3: Reinforcement Learning

![Reinforcement Learning](reinforcement_learning.jpg)

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

## System Architecture

---------------------

                                  +---------------+

                                  |  Camera Feeds  |

                                  +---------------+

                                         |

                                         |

                                         v

                                  +---------------+

                                  |  Traffic Monitoring  |

                                  |  (Computer Vision)    |

                                  +---------------+

                                         |

                                         |

                                         v

                                  +---------------+

                                  |  Graph Neural Network  |

                                  |  (Traffic Network Modeling) |

                                  +---------------+

                                         |

                                         |

                                         v

                                  +---------------+

                                  |  Reinforcement Learning  |

                                  |  (Traffic Signal Optimization) |

                                  +---------------+

----------------------

## Getting Started

-------------------

### 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

----------

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

## Future Work

--------------

* 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

---------

This project is licensed under the MIT License. See [LICENSE](LICENSE) for details.

## Acknowledgments

----------------

* 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

--------------

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