The Propensity to Cycle Tool: An open source online system for sustainable transport planning

Abstract

Getting people cycling is an increasingly common objective in transport planning institutions worldwide. A growing evidence base indicates that high quality infrastructure can boost local cycling rates. Yet for infrastructure and other cycling measures to be effective, it is important to intervene in the right places, such as along 'desire lines' of high latent demand. is creates the need for tools and methods to help answer the question 'where to build?' . Following a brief review of the policy and research context related to this question, this paper describes the design, features and potential applications of such a tool. e Propensity to Cycle Tool (PCT) is an online, interactive planning support system that was initially developed to explore and map cycling potential across England (see www.pct.bike). Based on origin-destination data it models cycling levels at area, desire line, route and route network levels, for current levels of cycling, and for scenario-based 'cycling futures.' Four scenarios are presented, including 'Go Dutch' and 'Ebikes, ' which explore what would happen if English people had the same propensity to cycle as Dutch people and the potential impact of electric cycles on cycling uptake. e cost effectiveness of investment depends not only on the number of additional trips cycled, but on wider impacts such as health and carbon benefits. e PCT reports these at area, desire line, and route level for each scenario. e PCT is open source, facilitating the creation of scenarios and deployment in new contexts. We conclude that the PCT illustrates the potential of online tools to inform transport decisions and raises the wider issue of how models should be used in transport planning.

Predictive spatial network analysis for high-resolution transport modeling, applied to cyclist flows, mode choice, and targeting investment

“…The latter two studies are successful at predicting mode choice (r 2 D 0.81) and flows (r 2 D 0.60), respectively, but the models are not sensitive to the precise location of infrastructure in relation to route choice and hence mode choice, and thus will ultimately be limited in their ability to suggest optimal locations for new infrastructure. An alternative approach (Lovelace et al, 2016) is to model potential rather than predictions, where potential is defined as travel demand over distances short enough to be cycled. These models are valuable for identifying potential at coarse spatial level but once that has been established, a different model is needed to predict the effect of spatially detailed infrastructure changes.…”

Cycling promotion schemes and long-term behavioural change: A case study from the University of Sheffield

“…Various route-allocation methods were tested, the most promising of which seemed to be the use of the CycleStreets.net API (see Lovelace et al, 2015): simpler 'shortest path' algorithms produced seemingly unrealistic routes. However, we decided to use Euclidean distance in the end, because of the high correlation between route and network distance observed for Sheffield data based on CycleStreets.net ( Fig.…”

David Boyce and Huw Williams, Forecasting urban travel: Past, present and future

“…Following this worldview and building on my skills learned in Quantitative Geography or 'Geocomputation', my work focusses on applied modelling of travel behaviour. My current role as Lead Developer of the Department for Transport (DfT) funded Propensity to Cycle Tool (PCT) (Lovelace et al 2015) has awoken me to the importance of academic research for providing tools that will eventually lead to decisions affecting millions of lives. I this context the book is reviewed from a somewhat utilitarian perspective: how can it help researchers to create the evidence base needed to move out of the mess of car dominated and highly inefficient transport systems that have arisen since World War II?…”