• Introduction: the Propensity to Cycle Tool
• The impacts of transport infrastructure
• Methods
• Results and discussion

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)

"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) and the Local Cycling and Walking Infrastructure Plan (LCWIP)

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

• Government Target
• Gender Equality
• Go Dutch
• Ebikes

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

The extension of the M74 motorway = 'natural experiment':

• People who live near motorways seem to be less physically active (D. Ogilvie et al. 2006)
• And more car dependent (Prins et al. 2017)
  • Although no evidence of impacts on active travel

A recent review of impact assessment methods in the English context found that an increasingly wide range of methods and approaches were being used (Tajima and Fischer 2013):

• Environmental Impact Assessment (EIA)
• Strategic Environmental Assessment (SEA)
• Health Impact Assessment (HIA)
• Gender Impact Assessment (GIA)

• Equality Impact Assessment (EqIA)

• But overall not a huge amount of research in the area, particularly in relation to the impact on active travel:

• "there is very little empirical data on the impact of road transport interventions", aside from injury reduction estimates (Thomson et al. 2008)

3 stage methodology to identify parallel lines:

• Subset desire lines whose cetrepoints are far from the proposed route
• Segment the proposed route
• Calculate the angle of each route

Then keep only lines parallel to proposed route segments

This involved the development of a new R function:

From the R package stplanr

## function (l, angle, bidirectional = FALSE, absolute = TRUE) 
## {
##     if (is(object = l, "Spatial")) {
##         line_angles = line_bearing(l)
##     }
##     else {
##         line_angles = l
##     }
##     angle_diff = angle - line_angles
##     angle_diff[angle_diff <= -180] = angle_diff[angle_diff <= 
##         -180] + 180
##     angle_diff[angle_diff >= 180] = angle_diff[angle_diff >= 
##         180] - 180
##     if (bidirectional) {
##         angle_diff[angle_diff <= -90] = 180 + angle_diff[angle_diff <= 
##             -90]
##         angle_diff[angle_diff >= 90] = 180 - angle_diff[angle_diff >= 
##             90]
##     }
##     if (absolute) 
##         angle_diff = abs(angle_diff)
##     angle_diff
## }
## <bytecode: 0xfb68578>
## <environment: namespace:stplanr>

The same method!

• Divide each route into 3

• Break the train line into segments

Method of splitting the route into discrete segments using the line segment function from the stplanr R package (a) and cycling potential severed (b).

• How to integrate this work in existing tools?
• How to ensure maximum policy impact?
• How reproducibility can help?

• Thanks for listening - get in touch via r.lovelace@leeds.ac.uk or @robinlovelace