provided along particular routes and origin-destination data on travel to work, was used to rank key routes in the city in terms of their cycling potential. 

Larsen et al. (2013) created an area-based ‘prioritization index’, for Montreal, Canada. This was based on four variables: the area’s current level of cycling, its cycling potential (estimated based on the shortest path between the origin and destination of short car trips from a travel survey), the number of injuries to cyclists, and locations prioritised by current cyclists for improvement (Larsen et al., 2013). These four were aggregated to the level of evenly spread cells covering the study area. The resulting heat map was used to recommend the construction or upgrade of cycle paths on specific roads. 

Although the methods presented in preceding three paragraphs were developed in an academic context, most of the usable tools for estimating cycling potential are practitioner-led. Many of these may never have been documented in the academic literature, so the subsequent overview should not be regarded as comprehensive (see Kuzmyak et al., 2014 and subsequent documents that cite this report). 

The Analysis of Cycling Potential (ACP) tool was developed by Transport for London (2010) to produce heat maps of cycling potential across London, for all trip purposes. It is based a mode choice model of likelihood of trips being cycled and uses the characteristics of observed trips (e.g. time of day, characteristics of the traveller, distance) as the main input dataset. The results of the ACP have informed local cycling schemes, such as where to build new cycle hire stations but does not use origin-destination data so is less relevant for route-based interventions. 

A more localised approach is the Permeability Assessment Tool (PAT), which was developed by a transport consultancy Payne (2014). The PAT is based on the concept of ‘filtered permeability’, which means providing a more direct route to people cycling than driving (Melia, 2015). The PAT works by combining geographical data, including the location of popular destinations and existing transport infrastructure, with on-site audit data of areas 

