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🚑 Emergency Vehicle Routing System

This mini-project demonstrates how graph algorithms (BFS & DFS) can be applied to real-world problems like emergency vehicle routing in urban cities. It helps simulate how ambulances, fire trucks, or police vehicles can find the shortest path in a road network using Breadth-First Search (BFS) and compare it with Depth-First Search (DFS).

📌 Features

  • Visualize the city road network as a graph (nodes = locations, edges = roads).
  • Input start and goal locations through GUI.
  • BFS Path → Finds & animates the shortest path.
  • DFS Path → Finds & animates a possible path (not always shortest).
  • Best Path → Compares BFS vs DFS paths.
  • Ambulance animation showing the route step-by-step.

🛠️ Tools & Technologies

  • Language: Python 3.x

  • Libraries:

    • Tkinter → GUI development
    • NetworkX → Graph creation & visualization
    • Matplotlib → Graph plotting & animation
    • Collections (deque) → Efficient BFS queue

  • IDE: VS Code / PyCharm / Jupyter Notebook

  • OS: Windows / Linux / Mac

📂 Project Structure

📦 Emergency-Vehicle-Routing
 ┣ 📜 main.py              # Project source code
 ┣ 📜 README.md            # Project documentation
 ┗ 📂 screenshots          # Output screenshots

🏗️ System Architecture

  1. Graph Representation

    • Nodes → Locations (Hospital, School, Mall, Fire Station, etc.)
    • Edges → Roads between locations

  2. Algorithms

    • BFS → Finds shortest path in unweighted graphs
    • DFS → Explores a path (not always shortest)

  3. GUI Components

    • Start & Goal input fields
    • Buttons: BFS Path | DFS Path | Best Path | Clear
    • Ambulance route animation

▶️ How to Run

  1. Clone the repository

    git clone https://github.com/your-username/emergency-routing-system.git
cd emergency-routing-system

  2. Install dependencies

    pip install matplotlib networkx

  3. Run the project

    python main.py

📚 References

✅ Conclusion

This project shows the practical application of graph algorithms in real-life emergency routing. It highlights why BFS is preferred over DFS for shortest-path problems in unweighted graphs, while also providing an interactive and educational GUI simulation.