🚇 Driverless Metro Simulation (Route Optimization)

A project by Kübra Temel

📌 About the Project

This project simulates an autonomous metro system and optimizes routes using graph-based algorithms. It provides two main functionalities:

• Finding the Least Transfer Route using Breadth-First Search (BFS)
• Finding the Fastest Route using A (A-Star) Algorithm*

This simulation helps visualize how metro networks can be optimized for efficiency, reducing transfer times and overall travel duration.

🛠️ Technologies Used

• Python 🐍
• Jupyter Notebook 📓
• Graph Algorithms (BFS & A*)

📂 Project Structure

KubraTemel_MetroSimulation/
│── KubraTemel_MetroSimulation.ipynb # Jupyter Notebook with code & explanations
│── README.md # Project documentation

🚀 How to Run the Simulation

1. Clone the repository:

git clone https://github.com/kubtem/KubraTemel_MetroSimulation.git
cd KubraTemel_MetroSimulation

2. Open Jupyter Notebook:

jupyter notebook KubraTemel_MetroSimulation.ipynb

3. Run all cells to execute the simulation and test different metro routes! Algorithms Used

BFS for Least Transfer Route

• Uses queue-based traversal to explore paths level by level. • Prioritizes paths with the fewest line transfers rather than the shortest distance.

A* Algorithm for Fastest Route

• Uses priority queues to find the most time-efficient route. • Heuristic-based approach for faster pathfinding.

Example Metro Network

Stations and connections modeled in the project:

   (A) --5min-- (B) --7min-- (C) --3min-- (D)
    |                                     |
   10min                                 4min
    |                                     |
   (E) ----------------------------------  

Example test case:

route = metro.find_least_transfer_route("A", "D")
print("Least transfer route from A to D:", route)

📬 Contact

💡 Created by Kübra Temel 📧 Email: kubra.temel@outlook.it 🔗 GitHub: github.com/kubtem

This project is open-source and contributions are welcome! 🚀