Advanced Simulation Assignments

This project comprises Python code from three consecutive assignments completed in the course Advanced Simulation of the MSc programme Engineering and Policy Analysis at TU Delft. The model analyses Bangladesh's road network using Road Maintenance Management System (RMMS) data to compute vulnerability and criticality metrics.

Authors

• Alessandro Dell-Orto
• other author
• other author
• other author
• other author

Repository Structure

• model – Python scripts for data processing and calculation of network metrics.
• analysis – CSV files with ranked criticality and vulnerability scores.
• Images – Plots created by the scripts.
• Report – Final report of the third assignment.
• requirements.txt – Python dependencies.

The data directory referenced in the scripts is not included because the datasets are large.

Methods Overview

The workflow is divided over the three assignments:

1. Data collection and cleaning – Scripts such as data_pull.py, data_clean.py and bridge_condition_refactored.py parse the RMMS .htm files and generate processed CSV datasets.
2. Vulnerability and criticality computation – merging_vulnerability_roads.py combines hazard scores with road attributes, and Compute_metrics.py calculates normalized indicators for roads and bridges.
3. Reporting – Rankings of the most critical and vulnerable links are saved to the analysis folder and summarised in the report under Report/.

Usage

Install the required packages with:

pip install -r requirements.txt

Then run the scripts in model/ following the order described above. Adjust the paths to the RMMS data files locally.

Modifying Inputs

You can adjust hazard weights or road condition categories by editing the data and scripts inside model/.

Notes on Data

The raw and processed datasets are not included in this repository because of their size.

Acknowledgments

This project is based on the EPA133a - Advanced Simulation course at TU Delft.