Building Capacity for Data-Driven Urban Mobility
Keynote for the ITF-Eurostat Conference on Promoting Data-Driven Decision-Making
Institute for Transport Studies, University of Leeds
November 4, 2025
Introduction
- Who am I? Professor of Transport Data Science at the University of Leeds.
- My focus: Building open, reproducible, and policy-relevant transport planning tools.
- Today’s topic: Building capacity for data-driven decision-making in urban mobility.
- Why it matters for Southeast Europe: Supporting sustainable mobility goals and the path to EU collaboration.
Context: Southeast Europe
The Challenge: The Data-to-Policy Gap
We have more data than ever before, but are we using it effectively?
Common Barriers:
- Data Access: Data is often siloed, proprietary, or not in a usable format.
- Skills & Tools: Lack of training and accessible tools to analyse the data.
- Institutional Culture: Resistance to new methods and a disconnect between analysts and decision-makers.
- Procurement: Over-reliance on ‘black box’ commercial solutions.
A Solution: Open & Reproducible Workflows
Openness builds trust and empowers collaboration.
- Open Source: The code is free to use, modify, and share.
- Open Data: Public data is accessible to everyone.
- Open Methods: Methodologies are transparent and documented.
- Open Access: Research and educational materials are freely available.
- Community of Practice.
Source: Lovelace, Parkin, and Cohen (2020)
Open science for researchers
Open access to the publications
Open access to sample (synthetic if sensitive) data
Open access to the code
Fully reproducible paper published with documentation
Project deployed in tool for non-specialist use
Source: Braga et al. (2023)
Example community of practice: QGIS
Why is QGIS so strong?
Aside: Geographic distribution of QGIS interest
Pillar 1: Open Data & Standards
Good analysis starts with good data.
- Data Standards are Key:
- GTFS for public transport schedules.
- GBFS for shared micro-mobility (bikes, scooters).
- OpenStreetMap for detailed street network data.
- National Data Portals: A vital resource for official statistics and geographic data.
- Example: Sourcing road network data from OpenStreetMap for a whole country.
Pillar 2: Open Source Tools
Powerful, free, and adaptable tools for transport analysis.
- The R & Python Ecosystems: Mature, extensive libraries for data science, statistics, and visualisation.
- Example (R): The
stplanrpackage for transport planning and modelling. - Example (Python):
geopandasandpandanafor network analysis. - These tools can be adapted to local needs and data sources.
Pillar 3: Communities of Practice
Capacity building is not just about tools, it’s about people.
- A ‘Community of Practice’ is a group of people who “share a concern or a passion for something they do and learn how to do it better as they interact regularly” (Wenger 1998).
- Open source projects naturally foster these communities.
- Examples: rOpenSci, rOpenSpain, QGIS user groups.
- They provide support, share best practices, and drive innovation.
- Key takeaway: To build capacity, invest in building communities.
Case Study 1: National-Scale Planning (UK)
From local data to national strategy with plan.activetravelengland.gov.uk.
- Purpose: Prioritise billions of £ in investment for walking and cycling.
- How it works:
- Integrates dozens of open datasets (e.g., census, road safety, network data).
- Uses a transparent, open-source model (
pctR package). - Provides a web interface for planners across the country.
- Impact: A consistent, evidence-based approach to national transport planning.