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James Woodcock: Abstract and background reading

SHORT ABSTACT: The Propensity to Cycle Tool (PCT) is a widely used open source tool and model for estimating cycling potential and corresponding health and carbon impacts in England and Wales across multiple scenarios. It is primarily funded by the Department for Transport and developed by an academic team from the Universities of Cambridge, Leeds, and Westminster. It covers both cycle commuting and the journey to school. 

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Background:

The Propensity to Cycle Tool (PCT) is a freely available, interactive tool with data downloads and open source code to help prioritise cycle planning. It was initially launched in England in 2017 and based on adult commuting data. Since then it has been extended to Wales and includes cycling to school.  A recent move to individual-level modelling has involved creating and using a synthetic population representing all commuters in England and Wales. It has documented use by over 80 local authorities in England and Wales.

Methods:

The 2011 National School Census and 2011 population Census provides origin-destination data for all state-funded schools and trips to work in England and Wales. Using these data, we modelled propensity to cycle as a function of route distance and hilliness between home and school/ work. We generated scenarios, including ‘Go Dutch’ – in which the English were as likely to cycle as the Dutch, accounting for trip distance and hilliness. Other scenarios varied for cycling to school and to work. For commuting we include an E-bikes scenario estimating cycling potential with widespread use of e-bikes. We also developed two scenarios based on achieving the Government target of a doubling of cycling. Our new individual-level approach has allowed us to create a “Near Market” scenario  where age, gender, ethnicity, car ownership and area level deprivation also affect an individual’s likelihood of switching to cycling, alongside route distance and hilliness.

We estimated changes in the level of cycling, walking, and driving, and associated impacts on physical activity and carbon emissions. For cycle to work we also estimated change in premature mortality and sickness absence. 

Results:

In 2011, 1.8% of children cycled to school. If Dutch levels of cycling were reached, under the Go Dutch scenario, this would rise to 41.0%, a 22-fold increase. This would increase physical activity from school travel among pupils by 57%. These impacts would be substantially larger in secondary schools than primary schools.

Amongst adults cycle to work would increase from 3.1% to 18.9%, with a 940 premature deaths averted each year and nearly 10,000 person years of sickness absence avoided. This equates to a health economic benefit of nearly £2 billion per year. 

Discussion:

The PCT method uses modelled cycling potential rather than modelling the effect of interventions. The goal of this approach is to illustrate the level, spatial distribution, and impacts of cycling potential under alternative scenarios. This can be a starting point for evaluation of which interventions are likely to effective to achieve this. 

In our scenarios, we mostly use only trip distance and hilliness as these are more consistently related to cycling levels than e.g. age and gender, for which the direction varies between higher and lower cycling countries.

We do not directly model the impacts of interventions because of a limited evidence base and because a model calibrated on factors that affect cycling in the current population is unlikely to be robust for predicting how major changes.

 

BACKGROUND READING

https://www.sciencedirect.com/science/article/pii/S2214140518301257 

https://www.jstor.org/stable/26211742?seq=1#metadata_info_tab_contents 

 

Title image1 James 

Title image2 James