TDCA — Data Science and AI for Data-Driven Transport Planning
A lecture for Transport Data Collection and Analysis (TDCA) at the Institute for Transport Studies, University of Leeds.
- Online version: itsleeds.github.io/tdca
- Source code: github.com/itsleeds/tdca
About this lecture
This lecture bridges the gap between traditional data collection and modern data science and AI. It covers:
- What are Data Science and AI? — definitions, transport applications
- AI for Transport Planning — reproducible environments, AI-powered programming, agentic loops
- Why Reproducibility Matters — Excel vs. code, transparency in policy
- The Power of (AI-Powered) Programming — flexibility, scalability, AI as accelerator
- Tools of the Trade — Python, R, Quarto, AI coding assistants
- Interactive Visualisations — Streamlit, Plotly, Shiny, PCT
- Making the Most of Transport Data — data types, AI applications
Links
- TDS module website: itsleeds.github.io/tds
- TDCA module repository: github.com/itsleeds/tdca
- Reproducible project template: github.com/Robinlovelace/reproducible-project-template
- Geocomputation with R: r.geocompx.org
- R for Data Science: r4ds.hadley.nz