Future-Proof Transport Planning

Seminar at the University of St Andrews
Robin Lovelace
r.lovelace@leeds.ac.uk

University of Leeds

May 8, 2025

Contents

Background

  • My journey

Current work

  • Transport Data Science
  • Biclar
  • Network Planning Tool

Future ideas

Abstract

Transport planning has always been a complex and multi-disciplinary enterprise requiring wide-ranging skills and methods. Like many fields of research exposed to the data revolution, it is also fast-moving, meaning that it’s hard to know how to keep the work future-proof. This talk will explore the challenges and opportunities of future-proofing transport planning, focusing on the role of data science and open source software. It will draw my experience developing and deploying tools such as the Propensity to Cycle Tool for England and Wales (publicly available at www.pct.bike), the Network Planning Tool for Scotland (publicly available at www.npt.scot) and the Biclar tool for Portugal (publicly available at biclar.tmlmobilidade.pt). I will also outline some tools and techniques we have developed at the University of Leeds for working with origin-destination data.

My journey

Figure 1: Places I have lived for 1+ years

Where I’m from

Figure 2: Places where I spent a lot of time in Herefordshire
  • Scholarship to study Environmental Science and Management

Influential book “SEWTHA”, freely available at withouthotair.com (MacKay 2009)

Blog post in The Oil Drum

Engineers Without Borders (EWB)

Wind turbine group

Finished wind turbine

Interest in sustainable transport

Cargo bike and bike trailers in action, June 2010

My thesis

Source: https://etheses.whiterose.ac.uk/id/eprint/5027/

Spatial microsimulation

First proper job (🙏Mark Birkin) and first Leeds-based paperpreprint (Lovelace et al. 2014)

Side projects: Cycling uptake work for CyclingUK

Source: CyclingUK (formerly CTC) response to government’s Cycling Delivery Plan consultation, available online at cyclinguk.org.

Work commissioned by CyclingUK (previously CTC)

Work on the economic benefits of cycling nationwide with James Woodcock and Fiona Crawford (Crawford and Lovelace 2015)

Propensity to Cycle Tool (www.pct.bike)

Source: article in practitioner magazine (Lovelace 2016).

First Propensity to Cycle Tool paper published in an academic journal (Lovelace et al. 2017)

From research to web tool

Research impact

Source: leeds.ac.uk front page, 2017-03-17

4* Research Excellence Framework (REF) case study

Source: results2021.ref.ac.uk (Lovelace et al. 2023)

Internship in No. 10 Downing Street

Fellowship in collaboration with 10 Downing Street, ONS, Data Science Campus, ADRUK, ESRC from November 2021 until April 2023

Source: Press Release “No.10 Data Science Fellowship”

Source: “Packaging Code and Data for Reproducible Research: A Case Study of Journey Time Statistics.” Environment and Planning B Botta et al. (2024).

Active Travel England

Department for Transport's Data Science for Transport conference

2 year contract in the Civil Service from January 2023

My roles:

  • Recruit the team
  • Lead Data Scientist
  • Projects: plan.activetravelengland.gov.uk (formerly ATIP), SchoolRoutes

Source: photo taken May 2023 at the Department for Transport’s Data Science for Transport conference

Active Travel England - Alan Turing Institute grant

Transport Minister Jesse Norman testing out the Active Travel Infrastructure Planning (ATIP) tool

Photo credit: Danny Williams

plan.activetravelengland.gov.uk

Now deployed on gov.uk, allowing anyone to browse data and design new schemes (demo if time allows) 🎉 Credit: Dustin Carlino and team

My current role

  • Professor of Transport Data Science
  • Focus on high-impact research
  • Teaching
  • Grant funding
  • Trying to succeed in academia without retreating up an “ivory tower”, focus on impact, build a community

What is Transport Data Science?

What is data science?

A field “to optimize the service contracts and maintenance intervals for industrial products”? (Davenport and Patil 2012)

“Data Science = Statistics + Machine Learning” or “Statistics + Computing + Communication + Sociology + Management”? (Vybornova, 2025)

Transport Data Science and Reproducibility

Transport data science is a discipline that:

  • “transform[s] raw data into understanding, insight, and knowledge” (Wickham, Cetinkaya-Rundel, and Grolemund 2023)
  • With reproducible and therefore falsifiable, scientific and scalable code.
  • For better understanding of and interventions in transport systems

Reproducibility is a continuous variable (Peng 2011)

Why make your research (more) reproducible?

Source: Raff (2023)

  • Scientific rigour
  • Benefits to your future self
  • Benefits to others
  • Huge increase in potential for impact

Why not make your research reproducible?

  • Time
  • Know-how
  • Lack of permission
  • Software is not open
  • Data is not open access
  • Someone might use it in unethical ways
  • Someone might “steal” the work

Example of fully reproducible research

spanishoddata paper and associated package which is now part of rOpenSpain public benefit data science community (see ropenspain.github.io)

Network Planning Tool for Scotland

Network Planning Workspace

From open source to open access

“In essence ‘open access’ goes beyond ‘open source’ in that users are not only given the option of viewing (potentially indecipherable) source code, but are encouraged to do so, with measures taken in the software itself, and the community that builds it, to make it more user-friendly.””

Source: (Lovelace, Parkin, and Cohen 2020)

Principles for future research and practice

  • Faster
  • More gamified/responsive/accessible
  • More open/reproducible
  • Higher resolution
  • Bolder

Source: “Designing an E-Bike City” (Ballo, Raubal, and Axhausen 2024)

Idea 1: ActiveCount

Source: telraam.net

Idea 2: SchoolRoutes

Idea 3: NetGen

Image credit: “The crowd is the territory” (Anderson et al. 2018)

Learn more and get involved

  • Sign up to get a GitHub account
  • Ask questions about datasets and digital tools
  • Develop and share ideas
  • Find bugs, build solutions

A final plug: 2 day workshop 18th-19th September

Exciting news: tickets for the 2-day workshop I’m doing on Data Science for Transport Planning are now available from the University of Leeds. See details here: store.leeds.ac.uk/product-cata…

[image or embed]

— Robin Lovelace ((robinlovelace.bsky.social?)) May 7, 2025 at 8:37 AM

Thank you!

References

Anderson, Jennings, Robert Soden, Brian Keegan, Leysia Palen, and Kenneth M. Anderson. 2018. “The Crowd Is the Territory: Assessing Quality in Peer-Produced Spatial Data During Disasters.” International Journal of HumanComputer Interaction 34 (4): 295–310. https://doi.org/10.1080/10447318.2018.1427828.
Ballo, Lukas, Martin Raubal, and Kay W. Axhausen. 2024. “Designing an E-Bike City: An Automated Process for Network-Wide Multimodal Road Space Reallocation.” Journal of Cycling and Micromobility Research 2 (December): 100048. https://doi.org/10.1016/j.jcmr.2024.100048.
Botta, Federico, Robin Lovelace, Laura Gilbert, and Arthur Turrell. 2024. “Packaging Code and Data for Reproducible Research: A Case Study of Journey Time Statistics.” Environment and Planning B: Urban Analytics and City Science 52 (4): 1002–13. https://doi.org/10.1177/23998083241267331.
Crawford, F., and R. Lovelace. 2015. “The Benefits of Getting England Cycling.” http://www.ctc.org.uk/sites/default/files/1501_fcrawford-rlovelace_economic-cycle-reformatted.pdf.
Davenport, Thomas H., and D. J. Patil. 2012. “Data Scientist: The Sexiest Job of the 21st Century.” Harvard Business Review, October. https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century.
Lovelace, Robin. 2016. “Mapping Out the Future of Cycling.” Get Britain Cycling 5: 2224. http://eprints.whiterose.ac.uk/100080/.
Lovelace, Robin, M Birkin, Joseph Talbot, and Malcolm Morgan. 2023. “Cycle Network Policy, Planning and Investment Transformed by the Propensity to Cycle Tool.” https://results2021.ref.ac.uk/impact/847d1191-7f25-46ba-a399-b481125edc8f?page=1.
Lovelace, Robin, Anna Goodman, Rachel Aldred, Nikolai Berkoff, Ali Abbas, and James Woodcock. 2017. “The Propensity to Cycle Tool: An Open Source Online System for Sustainable Transport Planning.” Journal of Transport and Land Use 10 (1). https://doi.org/10.5198/jtlu.2016.862.
Lovelace, Robin, Nick Malleson, Kirk Harland, and Mark Birkin. 2014. “Geotagged Tweets to Inform a Spatial Interaction Model: A Case Study of Museums.” https://doi.org/10.48550/ARXIV.1403.5118.
Lovelace, Robin, John Parkin, and Tom Cohen. 2020. “Open Access Transport Models: A Leverage Point in Sustainable Transport Planning.” Transport Policy 97 (October): 47–54. https://doi.org/10.1016/j.tranpol.2020.06.015.
MacKay, David J C. 2009. Sustainable Energy Without the Hot Air. Cambridge: UIT.
Peng, Roger D. 2011. “Reproducible Research in Computational Science.” Science (New York, N.y.) 334 (6060): 1226–27. https://doi.org/10.1126/science.1213847.
Raff, Edward. 2023. “Does the Market of Citations Reward Reproducible Work?” In, 8996. ACM REP ’23. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3589806.3600041.
Wickham, Hadley, Mine Cetinkaya-Rundel, and Garrett Grolemund. 2023. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 2nd edition. O’Reilly Media.

Plug: The A/B Street LTN tool a-b-street.github.io

Plug: Kay Axhausen’s upcoming talk at ITS

Source: ticketsource.com