into cycling potential, highlighting the following prominent levels (the PCT best fits into the third). 

• Area-based measures are based primarily on data at the level of administrative zones, which will vary depending on context. Outputs from these measures can assist with the location of site-specific transport infrastructure such as cycle parking. 

• Individual-based measures are based on survey data, typically a household travel survey. These are not always geographically specific and tend to be used to identify and categorise demographic groups in relation to cycling, such as near-market or as warranting tailored interventions, such as targeted cycle training schemes. 

• Route-based measures use origin-destination data which can be used to create ‘desire lines’ and (using route allocation) estimates of existing and potential demand at each point on the road network. 

This work is reviewed in relation to the PCT below and summarised in Table 1. 

Parkin et al. (2008) presented an area-based measure of cycling potential using regression model to estimate the proportion of commuter trips cycled across wards in England and Wales. Factors associated with lower levels of cycling included road defects, high rainfall, hills and a higher proportion of ethnic minority and low-income inhabitants. Parkin et al. concluded that policy makers must engage with a mixture of physical and social barriers to promote cycling effectively, with the implication that some areas have lower barriers to cycling — and hence higher propensity to cycle — than others. 

Zhang et al. (2014) created an individual-based model of cycling potential to prioritise where to build cycle paths to “achieve maximum impacts early on”. The outputs of this model were aggregated to the level of 67 statistical zones in the study area of Belo Horizonte, Brazil, and used to generate a ‘usage intensity index’ for potential cycle paths. This, combined with survey data on cyclists’ stated preferences on whether people would cycle were infrastructure 

