be representative of citizens and allow independent participation . Although these criteria depend primarily on how the tool is deployed in practice (for example with regards to training resources and workshops facilitated by planning authorities to ensure that the tool the tool is used effectively), which are outside the scope of the current paper, the PCT certainly encourages transparency through its use of open data and open source software and allows independent participation through its publicly accessible online interface. In addition to the international policy and academic context, the PCT was influenced by the national context. It was commissioned by the UK’s Department for Transport to identify “parts of [England] with the greatest propensity to cycle” (Department for Transport, 2015). Thus the aim was not to produce a full transport demand or land use model, but to provide an evidence base to prioritise where to create and improve cycling infrastructure based on scenarios of change. Local and national cycling targets are often based on a target mode share by a given date. 1 However, there is little evidence about what this might mean for cycling volumes along specific routes. The PCT tackles this issue by estimating rate of cycling locally under different scenarios and presenting the results on an interactive map. Its key features include: $^{1}$The local target in Bristol, for example, is for 20% of commuter trips to be cycled by 2020. Manchester (10% by 2025), Derbyshire (to double the number of people cycling by 2025) and London (to ‘double cycling’ by 2025) provide further examples of local ambitious time-bound cycling targets. • Visualisation of outputs at multiple geographic levels. The interactive map enables • Estimation of cycling potential at area, ‘desire line’ and route network levels. • Route-allocation of OD (origin-destination) pairs by a routing algorithm specifically developed for cycling. This was done by CycleStreets.net, a routing service developed by cyclists, for cyclists. 2.4 National context and features of the Propensity to Cycle Tool 9