🇨🇭 Swiss Transport Accessibility
📍 EPFL – Master in Data Science, Year 2 (2026)
👥 Team: Matthias Wyss, Ursula El-Khoury, Sarah Badr
🔗 Project Website: Demo
🎥 Video presentation: Video
📄 Final Report: Report
🔗 Code Repository: GitHub
This project explores the spatial accessibility of the Swiss public transport network, specifically focusing on the door-to-door travel time required to reach major InterCity (IC) railway hubs.
While Switzerland is renowned for its dense transport network, significant regional disparities exist. By layering multimodal public transport travel times with socioeconomic indicators, such as real estate prices, employment density, average salary, and passenger frequency, this interactive tool reveals how connectivity correlates with urban development and territorial inequalities.
The application was built using Python (with r5py) to compute massive travel-time matrices from GTFS and OSM data, and features two main frontend components:
- The Spatial Dimension (Map View): An interactive Leaflet.js map utilizing a 2km² grid-based encoding to display regional travel times. Users can toggle between multiple socioeconomic thematic layers and adjust departure times to visualize accessibility shifts.
- The Statistical Dimension (Analysis View): Coordinated D3.js charts (scatter plots, bar charts, and histograms) that move beyond geography to highlight abstract data relationships, allowing users to identify isolated hubs or undervalued regions.
🛠 Tools & Libraries:
- Python (Pandas, r5py)
- JavaScript (ES6+)
- HTML5 / CSS3
- Leaflet.js
- D3.js
- h3-js
- Jupyter Notebook
🧠 Techniques:
- Data Visualization
- Spatial & Geographic Analysis
- Multimodal Routing Analysis
- Interactive Mapping
- Coordinated Views