Infrabel Train Delay Analysis and ETCS Impact Report

This repository contains an analysis of train delays and incidents data from Infrabel, with a focus on the impact of the European Train Control System (ETCS). The project provides both a comprehensive analysis of train delays and the impact of incidents, and it demonstrates the effects of ETCS on punctuality and recovery times.

The analysis includes:

• Exploratory Data Analysis (EDA) with visualizations.
• Predictive modeling to assess the influence of ETCS on delays.
• A Streamlit dashboard that provides an interactive report.

All CSV datasets used in the analysis are provided in the repository, and all visualizations can be reproduced using the code in main.py. Additionally, pre-generated visualizations are included for easy setup of the Streamlit app.

Repository Structure

Infrabel-Data-Analysis/
│
├── __pycache__/                # Python cache files
├── myvenv/                     # Virtual environment
├── Data                        # All required datasets
├── analysis.py                 # Python file for analysis functions
├── preprocess.py               # Python file for data preprocessing functions
├── visualization.py            # Python file for visualizations
├── main.py                     # Main analysis script
├── streamlit.py                # Streamlit app script
├── Infrabel_Data_Analysis.ipynb # Jupyter Notebook with analysis
├── requirements.txt            # Python dependencies
├── Images/*.png                       # Pre-generated visualizations (for Streamlit app)
    └── *.html                      # Pre-generated HTML maps for the Streamlit app

Getting Started

1. Set Up a Virtual Environment

To get started with the project, you need to set up a Python virtual environment. Follow the steps below:

1. Clone the repository:

   git clone https://github.com/shreyab375/Infrabel-Data-Analysis.git
   cd Infrabel-Data-Analysis

2. Create a virtual environment:

   python3 -m venv myvenv

3. Activate the virtual environment:

   • On Windows:

     myvenv\Scripts\activate

   • On MacOS/Linux:

     source myvenv/bin/activate

2. Install Required Dependencies

After activating the virtual environment, install the required dependencies from the requirements.txt file:

pip install -r requirements.txt

3. Running the Python Code

You can execute the main analysis by running main.py. This will preprocess the datasets, perform analysis, generate visualizations, and build a predictive model.

python main.py

4. Running the Streamlit App

The interactive Streamlit app provides a report summarizing the results of the analysis with visualizations. To launch the app, execute:

streamlit run streamlit.py

This will open a web browser with the dashboard that includes:

• Visualizations showing delays, incidents, and ETCS impact.
• A summary of the key findings.
• Interactive maps and charts.

5. Using the Jupyter Notebook

For a step-by-step walkthrough of the analysis, you can also check the Jupyter notebook:

jupyter notebook Infrabel_Data_Analysis.ipynb

Key Features

• Delays Analysis: Provides insights into the relationship between delays, stopping places, and train lines.
• ETCS Impact: Demonstrates the benefits of ETCS in improving recovery times after incidents.
• Incident Hotspots: Identifies areas prone to incidents and their impact on delays.
• Predictive Modeling: Assesses the influence of different factors on train delays, including ETCS.

Datasets

• Regular data.csv: Contains regular train delay data.
• Incidents.csv: Incident data affecting train operations.
• ETCS.csv: Data regarding the deployment of ETCS across different lines.

Pre-Generated Visualizations

The repository includes several pre-generated visualizations (*.png and *.html files) to streamline the deployment of the Streamlit app. These visualizations include maps, charts, and figures illustrating the key insights from the analysis.