Introduction 

Getting people cycling is an increasingly common objective in transport planning institutions worldwide, and high-quality infrastructure has been proven to boost local cycling rates.

The Propensity to Cycle Tool (PCT) was designed to assist transportation planners and policy makers to prioritise investments and interventions to promote cycling. The PCT answers the question: ‘where is cycling currently common and where does cycling have the greatest potential to grow?’

 

First, the PCT is a strategic planning tool. Different visions of the future are represented through various scenarios of change, including the government’s draft Cycling Delivery Plan aims to double cycling in a decade. By showing what the rate of cycling could feasibly look like in different parts of cities and regions, and illustrating the associated increase in cycle use on the road network, the PCT should inform policies that seek a wider shift towards sustainable transport.

Second, the PCT can also be used at a smaller scale. The scenario level of commuter cycling along a particular road can be used to estimate future mode share for cycling on that corridor. This can be compared with the current allocation of space to different modes and used to consider reallocation from less sustainable modes to cater for cycling growth. In other cases, low current or potential flows may indicate a barrier, such as a major road or rail line, causing severance and lengthening trips. This could be addressed through new infrastructure such as a pedestrian and cycle bridge.

Central both to strategic and smaller-scale use is the question of where to prioritise high quality cycling infrastructure of sufficient capacity for a planned growth in cycling (Aldred et al 2016).

In summary, the PCT is a planning support system to improve cycling provision at many levels from regions to specific points on the road network. For further work on the approach, please see the paper on the PCT (Lovelace et al. 2015). To view the underlying source code, please visit Github/npct.

Data and methods

Large origin-destination datasets:

– 2011 Census data on journeys to work

– School Census data

– aggregations of mobile telephone datasets

For further information on the thinking underlying the tool’s design, and the methodology used to create it, please see (Lovelace et al. 2016

  • Funder: Department for Transport
  • Leeds lead: Robin Lovelace

Key findings

The outputs are, four scenarios estimating cycling potential down to the route network level. The visualisations / interactive maps are available publicly online: http://ww.pct.bike/

Value of the research

The tool is being used to design cycling strategies in many local authorities. Different visions of the future are represented through scenarios – including the government’s aim to double cycling in a decade and the more ambitious ‘Go Dutch’ scenario, whereby cycling levels are reached in England (allowing for English hilliness and trip distances). By showing what the rate of cycling could feasibly look like in different parts of cities and regions, and illustrating the associated increase in cycle use on the road network, the PCT should inform policies that seek a wider shift towards sustainable transport.

The PCT can also be used at a smaller scale, the scenario level of commuter cycling along a road can be used to estimate future mode share for cycling on that corridor. This can be compared with the current allocation of space to different modes of transport and used to consider reallocation from less sustainable modes to cater for cycling growth. In other cases, low current or potential flows may indicate a barrier, such as a major road or rail line, causing severance and lengthening trips. This could be addressed through new infrastructure such as a pedestrian and cycle bridge.

Researchers 

Principal Investigator: Dr James Woodcock, CEDAR, University of Cambridge
Co-investigator – Lead Data Analyst: Dr Anna Goodman, LSHTM
Co-investigator – Lead Developer: Dr Robin Lovelace, University of Leeds
Co-investigator – Lead Policy and Practice: Dr Rachel Aldred, University of Westminster
Data Scientist / Developer: Ali Abbas, CEDAR, University of Cambridge
Web Developer: Dr Nikolai Birkoff,
Data Scientist: Alvaro Ullrich, CEDAR, University of Cambridge