DSA-PROJECT

Overview

This project aims to facilitate efficient route finding within a network of charging stations for Electric Vehicles (EVs). It utilizes graph theory principles and Dijkstra's algorithm to determine the shortest path from a given starting point to each charging station, offering recommendations for the most efficient route based on distance and other relevant factors.

Setup Instructions

1. Install Python:

   • Ensure Python is installed on your system. If not, download and install it from python.org.

2. Install Required Libraries:

   • Open a terminal or command prompt and run:

     pip install networkx matplotlib
     

3. Download Data File:

   • Save your CSV data file (data.csv) in the same directory as your Python script.

Execution Instructions

1. Run the Script:

   • Open a terminal or command prompt.
   • Navigate to the directory containing your Python script (Project.py) and the CSV data file.

2. Execute the Script:

   • Run the Python script:

     python Project.py
     

3. Follow the Prompts:

   • Enter the current point (between A to W) when prompted.
   • The script will display the graph with charging stations highlighted and recommend the most efficient route considering the shortest distance to the stations.

Features

• Graph Construction: Constructs a weighted directed graph from the provided data file, representing nodes and edges with distances.
• Routing Algorithm: Implements Dijkstra's algorithm to find the shortest path from the starting node to each charging station.
• Route Recommendation: Analyzes computed paths to recommend the most efficient route to a charging station based on total distance.
• Graph Visualization: Utilizes NetworkX and Matplotlib to visualize the network graph, highlighting charging stations for better understanding.
• User Interaction: Provides a user-friendly interface prompting for the current node and displaying recommended routes.

Project Logs

1. Data Preparation:

   • Recorded node and edge information, along with distances between nodes.
   • Created a CSV file to represent the network data.
   • Loaded CSV data for interpretation in Python.

2. Graph Visualization:

   • Utilized NetworkX and Matplotlib libraries to visualize the graph.
   • Differentiated charging stations with distinct colors for clarity.
   • Labeled edge attributes, such as weight (distance), for better comprehension.

3. Dijkstra's Algorithm Implementation:

   • Implemented Dijkstra's algorithm to find the shortest path to each charging station from a given origin node.
   • Recorded paths, visited nodes, and distances traveled during iteration.
   • Calculated shortest distance and constructed the path to the charging station.

4. Efficiency Calculation:

   • Iterated through the list of EV stations to calculate the shortest path and total distance using Dijkstra's algorithm.
   • Recommended the shortest and nearest station based on calculated distances.

5. Main Functionality:

   • Executed the display function.
   • Prompted user input for the current node and displayed paths to each station, followed by recommending the best station to visit.

Resources

• NetworkX Documentation
• Matplotlib Documentation
• Python Tutorial on File Handling
• Dijkstra’s Algorithm
• Python Tutorial
• GeeksforGeeks