• Context: harnessing data for transport planning
• The Propensity to Cycle Tool (PCT)
• The Cycling Infrastructure Prioritisation Toolkit (CyIPT)

• Challenge: operationalise data
• Challenge: make locally specific

• Travel behaviour data
• Route network data
• Existing infrastructure (road widths, traffic, future possibilities)
• Road safety data
• Air pollution data
• Crowdsourced data

~200 km cycle network in Seville, Spain. Source: WHO report at ATFutures/who

• Not a UK-specific issue, but benefits of country-specific tools

• 3 years in the making
• Origins go back further
• "An algorithm to decide where to build next"!
• Internationalisation of methods (World Health Organisation funded project)

"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

Included in Cycling and Walking Infrastructure Strategy (CWIS)

• 12 month project funded by DfT's Innovation Challenge Fund (ICF)

• Aim: tackle the challenge that cycling uptake is often limited by infrastructural barriers which could be remediated cost-effectively, yet investment is often spent on less cost-effective interventions, based on assessment of only a few options.

• Project team:
  • Robin Lovelace (University of Leeds)
  • Malcolm Morgan (University of Leeds)
  • John Parkin (University of West of England)
  • Martin Lucas-Smith (Cyclestreets.net)
  • Adrian Lord (Phil Jones Associates)

• We can use 'backcasting' to estimate long-term potential under ideal questions (PCT)
• But transport authorities need forecasts of future uptake
• From specific interventions in order to do this
• There is much existing work on this
• But none that is 'operationalisable'

• How to operationalise available data?

• Clear link between infrastructure and uptake

• DfT's Transport Direct data
• 2001 OD data (manipulated and joined with 2011 data)

See: https://www.cyipt.bike (password protected)

• Online interfaces reduce barriers
• But there are benefits of running analysis locally
• Various software options, including:

• QGIS mapping software
• sDNA QGIS plugin
• R (see upcoming course 26th - 27th April)
• Key feature of CyIPT and PCT:
• Open source and provides open data downloads

• 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)

• The PCT in action: http://www.pct.bike/
• The CyIPT: http://cyipt.bike/
• A paper on the PCT (Lovelace et al 2017): https://www.jtlu.org/index.php/jtlu/article/view/862
• An article explaining R for transport planning with stplanr: cran.r-project.org