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)
  • 📃 Table of Contents
  • About the project
    • ℹ️ Project Description
    • 👥 Team Members
  • Getting Started
    • ⚙️ Prerequisites
    • 🚦 Run the Project
  • 🗂️ File Structure
  • 📝 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, which is a French CS engineering school. The subject is defined in the following repository: 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 for building a robust RESTful API, TortoiseORM for interacting with the PostgreSQL database, and advanced graph algorithms for calculations.
• Frontend: React with JavaScript and Leaflet for a dynamic and interactive user interface, allowing users to visualize the metro map and routes.

👥 Team Members

• P4ND4P0W3R - Paul HU
• RomainBilly0 - Romain BILLY
• LouisGodfrin - Louis GODFRIN
• CTauziede - Clément TAUZIEDE
• alexandre-bussiere - Alexandre BUSSIERE

Getting Started

⚙️ Prerequisites

• Backend:
  • Python 3.12 or higher
  • PostgreSQL
• Frontend:
  • Node.js 20 (LTS) or higher
  • npm or Yarn

🚦 Run the Project

1. Clone the repository:

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

2. Navigate to the project directory:

   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:

   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:

   deactivate

4. Setup and run the Frontend:

   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 (https://data.iledefrance-mobilites.fr/explore/dataset/offre-horaires-tc-gtfs-idfm/information/) 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.