https://github.com/p4nd4p0w3r/metro-efrei-dodo

This project leverages real-time data from Ile-de-France Mobilités (IDFM) to provide an efficient metro navigation system for Paris and Bordeaux (using a simplified dataset). It utilizes graph algorithms to calculate shortest paths, visualize the minimum spanning tree of the metro network, and check network connectivity.
https://github.com/p4nd4p0w3r/metro-efrei-dodo

2024 dijkstra-algorithm dijkstra-shortest-path efrei graph-algorithms it l3 mastercamp paris prim-algorithm real-data s6 subway transportation-network

Last synced: 2 months ago
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This project leverages real-time data from Ile-de-France Mobilités (IDFM) to provide an efficient metro navigation system for Paris and Bordeaux (using a simplified dataset). It utilizes graph algorithms to calculate shortest paths, visualize the minimum spanning tree of the metro network, and check network connectivity.

• Host: GitHub
• URL: https://github.com/p4nd4p0w3r/metro-efrei-dodo
• Owner: P4ND4P0W3R
• Created: 2024-06-06T09:11:54.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2025-01-28T09:43:51.000Z (3 months ago)
• Last Synced: 2025-01-28T10:30:40.517Z (3 months ago)
• Topics: 2024, dijkstra-algorithm, dijkstra-shortest-path, efrei, graph-algorithms, it, l3, mastercamp, paris, prim-algorithm, real-data, s6, subway, transportation-network
• Language: Python
• Homepage:
• Size: 365 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Metro, Efrei, Dodo (MED)

This project leverages real-time data from Ile-de-France Mobilités (IDFM) to provide an efficient metro navigation system for Paris and Bordeaux (using a simplified dataset). It utilizes graph algorithms to calculate shortest paths, visualize the minimum spanning tree of the metro network, and check network connectivity.

## 📃 Table of Contents

- [Metro, Efrei, Dodo (MED)](#metro-efrei-dodo-med)

  - [📃 Table of Contents](#-table-of-contents)

  - [About the project](#about-the-project)

    - [ℹ️ Project Description](#ℹ️-project-description)

    - [👥 Team Members](#-team-members)

  - [Getting Started](#getting-started)

    - [⚙️ Prerequisites](#️-prerequisites)

    - [🚦 Run the Project](#-run-the-project)

  - [🗂️ File Structure](#️-file-structure)

  - [📝 Additional Notes](#-additional-notes)

## About the project

> [!NOTE]

> This project falls within the scope of the **Solution Factory** for the 2024 IT Mastercamp during our third year curriculum at [EFREI](https://www.efrei.fr/), which is a French CS engineering school. The subject is defined in the following repository: [ossef/Solution_Factory_IT](https://github.com/ossef/Solution_Factory_IT).

### ℹ️ Project Description

This application allows users to:

- **Find the shortest path:** Determine the fastest route between two metro stations, considering real-time schedules and transfer times.

- **Visualize the minimum spanning tree (MST):** Explore the underlying structure of the metro network using Kruskal's algorithm, understanding the most efficient connections.

- **Check network connectivity:** Verify if the entire metro network is reachable from any given station on a specific date.

The project uses:

- **Backend:** Python with [FastAPI](https://fastapi.tiangolo.com/) for building a robust RESTful API, [TortoiseORM](https://tortoise.github.io/toc.html) for interacting with the PostgreSQL database, and advanced graph algorithms for calculations.

- **Frontend:** [React](https://react.dev/) with JavaScript and [Leaflet](https://react-leaflet.js.org/) for a dynamic and interactive user interface, allowing users to visualize the metro map and routes.

### 👥 Team Members

- [P4ND4P0W3R](https://github.com/P4ND4P0W3R) - Paul HU

- [RomainBilly0](https://github.com/RomainBilly0) - Romain BILLY

- [LouisGodfrin](https://github.com/LouisGodfrin) - Louis GODFRIN

- [CTauziede](https://github.com/CTauziede) - Clément TAUZIEDE

- [alexandre-bussiere](https://github.com/alexandre-bussiere) - Alexandre BUSSIERE

## Getting Started

### ⚙️ Prerequisites

- **Backend:**

  - [Python 3.12 or higher](https://www.python.org/downloads/)

  - [PostgreSQL](https://www.postgresql.org/)

- **Frontend:**

  - [Node.js 20 (LTS) or higher](https://nodejs.org/en)

  - [npm](https://www.npmjs.com/) or [Yarn](https://yarnpkg.com/)

### 🚦 Run the Project

1. **Clone the repository:**

    ```bash

    git clone https://github.com/P4ND4P0W3R/Metro-Efrei-Dodo.git

    ```

2. **Navigate to the project directory:**

    ```bash

    cd Metro-Efrei-Dodo

    ```

> [!IMPORTANT]

> Before running the backend, create a `.env` file in the `backend/` directory and add your database connection string as `DATABASE_URL=postgres://username:password@host:port/db_name`.

3. **Setup and run the Backend:**

    ```bash

    python -m venv .venv  # Create a virtual environment

    .venv/Scripts/activate  # Activate the virtual environment

    pip install -r ./backend/requirements.txt  # Install dependencies from requirements.txt

    python populate_database.py  # (OPTIONAL) Populate database with GTFS data (adjust paths if necessary)

    cd backend/app

    uvicorn main:app --reload   # Start the FastAPI server

    ```

    When you are done, you can deactivate your virtual environment:

    ```bash

    deactivate

    ```

4. **Setup and run the Frontend:**

    ```bash

    cd frontend

    npm install  # or yarn install

    npm run dev  # or yarn dev

    ```

Now you should be able to access the frontend application in your browser at `http://localhost:5173` and interact with the FastAPI backend running at `http://localhost:8000`.

## 🗂️ File Structure

- **backend:**

  - `.venv/`: The virtual environment directory containing isolated Python packages.

  - `main.py`: The primary entry point for the FastAPI application.

  - `config.py`: Configuration for the database and other settings.

  - `models.py`: Defines the database models using TortoiseORM.

  - `services/`: Contains logic for specific features (graph, connectivity, MST).

  - `utils/`: Holds general utility functions.

  - `tests/`: Contains unit and integration tests for the backend.

  - `requirements.txt`: Lists the required Python packages for the backend.

- **frontend:**

  - `src/`: The main source code for the React frontend application.

  - `public/`: Holds static files like your `index.html`.

  - `package.json`: Defines frontend dependencies and scripts.

## 📝 Additional Notes

- **GTFS Data:** You will need to download the latest GTFS data from IDFM () and place it in the appropriate directory (e.g., `backend/data/raw_gtfs/`).

    You process the files to only keep the `RATP` lines by using the command `python backend/app/utils/data_processing.py` in the `root` directory.

- **Database:** Ensure that your PostgreSQL database is set up correctly with the credentials defined in your `.env` file.

- **Leaflet:** Customize the Leaflet map in your frontend component to match the geographic region you're working with.

This project aims to provide a flexible and scalable foundation for a metro navigation application. You can extend it with additional features like:

- **Real-time train information:** Integrate live updates on train delays and disruptions.

- **Multimodal routing:** Incorporate other modes of transportation (bus, tram, etc.) into the pathfinding.

- **User accounts:** Allow users to save favorite routes, set journey preferences, and receive personalized notifications.

- **Accessibility:** Highlight accessible stations and routes.

> [!IMPORTANT]

>

> - The `.venv` folder should be added to your `.gitignore` to avoid unnecessary tracking.

> - This project is for educational purposes and should not be considered a production-ready system.

> - Ensure you have the correct GTFS files and data processing scripts.