2017-05-25. Slides: rpubs.com/RobinLovelace.

Contents

  • Context
  • Recent additions to the PCT
  • Conclusion
  • Question: who has heard of the PCT?
  • Who has used the PCT?

Context

Motivation

Source: Warrington cycle campaign

  • Sub-optimal resource allocation

Prior work (source: Lovelace et al. 2017)

Tool Scale Coverage Public access Format of output Levels of analysis Software licence
Propensity to Cycle Tool National England Yes Online map A, OD, R, RN Open source
Prioritization Index City Montreal No GIS-based P, A, R Proprietary
PAT Local Parts of Dublin No GIS-based A, OD, R Proprietary
Usage intensity index City Belo Horizonte No GIS-based A, OD, R, I Proprietary
Bicycle share model National England, Wales No Static A, R Unknown
Cycling Potential Tool City London No Static A, I Unknown
Santa Monica model City Santa Monica No Static P, OD, A Unknown

The PCT team

"If you want to go far, go as a team"

Robin Lovelace (Lead Developer, University of Leeds)

  • James Woodcock (Principal Investigator, Cambridge University)
  • Anna Goodman (Lead Data Analyst, LSHTM)
  • Rachel Aldred (Lead Policy and Practice, Westminster University)
  • Ali Abbas (User Interface, University of Cambridge)
  • Alvaro Ullrich (Data Management, University of Cambridge)
  • Nikolai Berkoff (System Architecture, Independent Developer)

  • Malcolm Morgan (GIS and infrastructure expert, UoL)

  • Academic write-up (Lovelace et al. 2017)

Policy feedback

"The PCT is a brilliant example of using Big Data to better plan infrastructure investment. It will allow us to have more confidence that new schemes are built in places and along travel corridors where there is high latent demand."

  • Shane Snow: Head of Seamless Travel Team, Sustainable and Acessible Travel Division

The PCT in CWIS and LCWIP

Included in Cycling and Walking Infrastructure Strategy (CWIS) and the Local Cycling and Walking Infrastructure Plan (LCWIP)

How the PCT works

Shows on the map where there is high cycling potential, for 4 scenarios of change

  • Government Target
  • Gender Equality
  • Go Dutch
  • Ebikes

Scenario shift in desire lines

Source: Lovelace et al. (2017)

  • Origin-destination data shows 'desire lines'
  • How will these shift with cycling uptake

Scenario shift in network load

A live demo for Leeds

"Actions speak louder than words"

Recent additions to the PCT

Travel to schools layer

Prototype online

Overlaying propensity to cycle to school and work

New LSOA layer (Morgan et al. in Press)

Where to prioritise? (CyIPT)

  • Cycling Infrastructure Prioritisation Toolkit (CyIPT): DfT-funded toolkit for cycling infrastructure prioritisation
  • Combines many datasets ("PCT + Infra") to identify 'low hanging fruit'

Next steps

Many potential directions for future work

But priority is still on impact

  • Case study of usage in specific contexts
  • Impact on transport planning approachs
  • But whole network design found to be important (Buehler and Dill 2016)
  • Shift in focus: from where to what to build
  • Internationalisation

References

Buehler, Ralph, and Jennifer Dill. 2016. “Bikeway Networks: A Review of Effects on Cycling.” Transport Reviews 36 (1): 9–27. doi:10.1080/01441647.2015.1069908.

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, December. doi:10.5198/jtlu.2016.862.