Show simple item record

dc.contributor.authorWoodcock, James
dc.contributor.authorAbbas, Ali
dc.contributor.authorUllrich, Alvaro
dc.contributor.authorTainio, Marko
dc.contributor.authorLovelace, Robin
dc.contributor.authorSá, Thiago H
dc.contributor.authorWestgate, Kate
dc.contributor.authorGoodman, Anna
dc.description.abstractBACKGROUND: 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 (NTS), 2004-2014 sample, 132,000 adults. 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 impact on travel patterns, health, and greenhouse gas emissions. Estimates of nonoccupational physical activity are generated by fusing the NTS with the English Active People Survey (APS, 2013-2014, 559,515 adults) to create a synthetic population. Under 'equity' scenarios, we investigate what would happen if cycling levels increased 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, 2013-2014 Dutch Travel Survey (50,868 adults).Outcomes are presented across domains including transport (trip duration and trips by mode), health (physical activity levels, years of life lost), and car transport-related CO2 emissions. Results can be visualised for the whole population and various subpopulations (region, age, gender, and ethnicity). The tool is available at 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 would be similar, while the reduction in car miles and CO2 emissions would be 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%). 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). 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. Future work should investigate the sensitivity of results to assumptions and omissions, and if this varies across setting.
dc.publisherPublic Library of Science (PLoS)
dc.rightsAttribution 4.0 International
dc.subjectEnvironmental Pollutants
dc.subjectRisk Assessment
dc.subjectRisk Factors
dc.subjectGreenhouse Effect
dc.subjectEnvironmental Pollution
dc.subjectEnvironmental Monitoring
dc.subjectHealth Status
dc.subjectTime Factors
dc.subjectMiddle Aged
dc.subjectYoung Adult
dc.subjectProtective Factors
dc.subjectHealthy Lifestyle
dc.subjectGreenhouse Gases
dc.titleDevelopment of the Impacts of Cycling Tool (ICT): A modelling study and web tool for evaluating health and environmental impacts of cycling uptake.
prism.publicationNamePLoS Med
dc.contributor.orcidWoodcock, James [0000-0003-4769-5375]
dc.contributor.orcidTainio, Marko [0000-0002-0973-2342]
dc.contributor.orcidSá, Thiago H [0000-0002-9348-4483]
dc.contributor.orcidWestgate, Kate [0000-0002-0283-3562]
rioxxterms.typeJournal Article/Review
pubs.funder-project-idWellcome Trust (087636/Z/08/Z)
pubs.funder-project-idEconomic and Social Research Council (ES/G007462/1)
pubs.funder-project-idMedical Research Council (MR/K023187/1)
pubs.funder-project-idMedical Research Council (MR/P02663X/1)
pubs.funder-project-idMedical Research Council (MC_UU_12015/3)
pubs.funder-project-idNational Institute for Health Research (NIHRDH-IS-BRC-1215-20014)

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International