2018-05-01. Slides: rpubs.com/RobinLovelace.

Outline

  • Context
  • The Propensity to Cycle Tool
  • Tools to prioritise infrastucture

The scale of the challenge, from this…

Towards cycling being the natural choice

Problem: evidence overload

  • Problem is operationalising this data
  • Needs to be provided in a format that can be acted on at the local level

The Propensity to Cycle Tool - see www.pct.bike

The front page of the open source, open access Propensity to Cycle Tool (PCT).

The front page of the open source, open access Propensity to Cycle Tool (PCT).

Context: from concept to implementation

  • 3 years in the making
  • Origins go back further
Concept (PhD) -> Job at UoL (2009 - 2013)
 Discovery of R programming and shiny (2013)
  Link-up with Cambridge University and colleagues (2015)
    Implementation on national OD dataset, 700k routes (2016)
      Completed LSOA phase (4 million lines!) (2017)
        PCT Wales commissioned + CyIPT Phase I (2018)
  • 2018: PCT Phase III
  • A multi-university project

The academic landscape (see Lovelace et al. 2017)

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
Cycling Potential Tool City London No Static A, I Unknown
Santa Monica model City Santa Monica No Static P, OD, A Unknown

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 shows the country’s great potential to get on their bikes, highlights the areas of highest possible growth and will be a useful innovation for local authorities to get the greatest bang for their buck from cycling investments  and realise cycling potential."

  • Andrew Jones, Parliamentary Under Secretary of State for Transport

The PCT in CWIS

Included in Cycling and Walking Infrastructure Strategy (CWIS)

A model of cycling uptake

  • Hilliness and distance are (relatively) unchanging over time
  • Model based on polynomial logit model of both:

\[ logit(pcycle) = \alpha + \beta_1 d + \beta_2 d^{0.5} + \beta_3 d^2 + \gamma h + \delta_1 d h + \delta_2 d^{0.5} h \]

logit_pcycle = -3.9 + (-0.59 * distance) + (1.8 * sqrt(distance) ) + (0.008 * distance^2)

Tools to prioritise cycling infrastructure. How many cyclists result from a new cycle path?

At low geographic resolution

  • Clear link between infrastructure and uptake, but inconsistent

Why investigate it?

  • We can use 'backcasting' to estimate long-term potential under ideal questions
  • But transport authorities need forecasts of future uptake
  • From specific interventions in order to do this
  • There is much existing work on this
  • But not able to automatically estimate uptake resulting from new infrastructure
  • A $64,000 question

Breakthrough datasets

How to model infrastructure -> cycling?

Where to build what?

Thanks + links!