Development of the Impacts of Cycling Tool (ICT): A modelling study and web tool for evaluating health and environmental impacts of cycling uptake.

Abstract

Background A modal shift to cycling has the potential to reduce greenhouse gas emissions, and provide health co-benefits. Methods, models, and tools are needed to estimate the potential for cycling uptake and communicate to policy makers the range of impacts this would have. Methods and Findings The Impacts of Cycling Tool (ICT) is an open source model with a web interface for visualising travel patterns and comparing the impacts of different scenarios of cycling uptake. It is currently applied to England The ICT allows users to visualise individual and trip-level data from the English National Travel Survey. It models scenarios in which there is an increase in the proportion of the population who cycle regularly, using a distance-based propensity approach to model which trips would be cycled. From this, the model estimates likely impacts on travel patterns, health, and greenhouse gas emissions. Estimates of non-occupational physical activity are generated by fusing the National Travel Survey with the English Active People Survey, to create a synthetic population. Under ‘equity’ scenarios, we investigate what would happen if cycling levels increase equally among all age and gender categories, as opposed to in proportion to the profile of current cyclists. Under electric assist bike (pedelecs or ‘e-bike’) scenarios, the probability of cycling longer trips increases, based on the e-bike data from the Netherlands. Outcomes are presented across domains including transport (trip duration and mode shift), health (physical activity levels, years of life lost), and car transport related CO2 emissions. Results can be visualised for the whole population and various sub-populations (region, age, gender, and ethnicity). The tool is available at www.pct.bike/ict If the proportion of the English population who cycle regularly increased from 4.8% to 25% then there would be notable reductions in car miles and passenger related CO2 emissions (2.2%), and health benefits (2.1% reduction in years of life lost due to premature mortality). If the new cyclists had access to e-bikes then mortality reductions were similar, while the reduction in car miles and CO2 emissions was larger (2.7%). If take-up of cycling occurred equally by gender and age (under 80 years) then health benefits would be marginally greater (2.2%) but reduction in CO2 slightly smaller (1.8%). Conclusion This study demonstrates a generalisable approach for using travel survey data to model scenarios of cycling uptake that can be applied to a wide range of settings. The use of individual level data allows investigation of a wide range of outcomes, and variation across subgroups. The study is limited by the quality and comparability of the input data (including reliance on self-report behaviours). As with all modelling studies many assumptions are required, and potentially important pathways excluded (e.g. injury, air pollution, and noise pollution). Future work should investigate the sensitivity of results to these assumptions and omissions, and if this varies across setting.