• Context
• Scenarios of active travel uptake
• How many cyclists does a new cycle path create?
• Scenarios of infrastructure change
• Discussion

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

• 3 years in the making
• Origins go back further

Concept (PhD) -> Job at UoL (2009 - 2013)
 Discovery of R programming and shiny (2013)
  'Propensity to Cycle' bid by DfT via SDG (2014)
    Link-up with Cambridge University and colleagues (2015)
     Implementation on national OD dataset, 700k routes (2016)
      Completed LSOA phase (4 million lines!) (2017)

• 2018: (Global PCT?)

• A multi-university 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)

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

• Government Target
• Gender Equality
• Go Dutch
• Ebikes

• 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)

• Test version of pct.bike

• 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

• Preliminary results

## lm(formula = p_uptake ~ dist + exposure, data = l, weights = all11)
## 
## Weighted Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1158 -0.3579 -0.0184  0.2821  4.5564 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.372e-02  4.207e-03   5.639 2.28e-08 ***
## dist        -1.671e-07  8.424e-07  -0.198  0.84283    
## exposure     4.147e-02  1.523e-02   2.724  0.00658 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7972 on 906 degrees of freedom
## Multiple R-squared:  0.008318,   Adjusted R-squared:  0.006128 
## F-statistic: 3.799 on 2 and 906 DF,  p-value: 0.02274

• Over to Malcolm

stplanr lives here: https://github.com/ropensci/stplanr

Package can be installed from CRAN or GitHub (see the package's README for details), it can be loaded in with library():

install.packages("stplanr") # stable CRAN version
# devtools::install_github("ropensci/stplanr") # dev version

This talk will provide an overview of the work that Robin Lovelace and Malcolm Morgan (ITS) have been doing as part of their Department for Transport funded projects on the Propensity to Cycle Tool (PCT, which has become part of UK government policy in the Cycling and Walking Infrastructure Strategy) and follow-on work on the Cycling Infrastructure Prioritisation Toolkit (CyIPT). Although strong evidence shows that infrastructure usually precedes (and to some extent causes) behaviour change the starting point of the talk will be behaviour: how do people currently get around and how could it be different, based on the fundamentals of route distance and hilliness. Robin will demonstrate the PCT in action, talk about the R package stplanr that he developed to develop it, and outline plans for a globally scalable transport planning toolkit that builds on the PCT work.

Following this high-level overview Malcolm will zoom into the detail: How the CyIPT identifies the best places for infrastructure change and what that infrastructure should be. He will also talk about the advanced programming techniques needed to process such complex geospatial network data at city to national levels.

There is a clear linkage between the behaviour and infrastructure focci of Robin and Malcolm's talks that will become aparent as the seminar progresses.

Links to check before the talk:

• The PCT in action: http://www.pct.bike/
• A prototype of 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 stplanr (for people interested in the tech): https://cran.r-project.org/web/packages/stplanr/vignettes/introducing-stplanr.html