Internationally Scalable and Publicly Accessible Tools for Prioritising Investment in Active Travel

Background In parallel with the growing evidence base supporting modal shift to walking and cycling for health and environmental reasons, tools for prioritizing interventions to encourage active travel have evolved. Increasingly detailed datasets provide researchers with insights into how transport systems operate and change, at levels of geographical and temporal resolution that were unavailable a few decades ago. Used in combination with new digital technologies and modelling techniques, such evidence can be made publicly accessible via interactive on-line 'web apps'. This paper explores the potential of such emerging tools to improve and democratize transport decision making for healthier outcomes, with a focus on the development of Propensity to Cycle analysis for Seville, Spain. Methods The National Propensity to Cycle Tool for England (www.pct.bike) is a freely-available online tool commissioned by the UK's Department for Transport to improve the effectiveness of interventions to get people cycling. This paper reports the application of some of the same methods at the city-level in a new context, with a case study of Seville. We report the preparation of Open Street Map (OSM) transport network data, the aggregation of geographical datasets into and origin-destination (OD) matrix of appropriate spatial resolution, and the estimation of current cycling levels and the potential for growth at desire line and route network levels. Results It was found that publicly accessible tools to help prioritize investment in walking and cycling are increasingly viable, even in contexts where detailed OD data (reporting mode split at the desire line level) is absent. The case study of Seville shows how open source software can be used to create reproducible transport planning workflows, the results of which can be made accessible online for public scrutiny and exploration. The results show that there is substantial cycling potential outside of the central area where cycling infrastructure has been constructed, providing an evidence-base for extending Seville’s cycling network into the city’s suburbs. Conclusions The case study of taking ‘propensity to cycle’ methods developed in England and applying them to Seville shows the potential for uptake of open source and open access tools for designing healthier transport system. New digital technologies, if used appropriately and building on the best available evidence, can empower researchers, planners and public with the evidence-based needed to design effective interventions for active travel.