We have provided new area level datasets, showing the cycling potential (using the Ebikes scenario) just for those trips that come from public transport. Local authority level dataset here; LSOA-level dataset set here.
The Propensity to Cycle Tool (PCT) models what cycling in England and Wales could look like under different scenarios of cycling uptake. The two scenarios that show best the full scale of national cycling potential are the Go Dutch and the Ebike scenarios.
In the Go Dutch commute layer we show what cycling levels in England and Wales could look like, including as counts on the transport network, if people here were as likely to cycle to work as people in the Netherlands, allowing for trip distance and hilliness. The Ebikes scenario does a similar calculation but assumes relatively widespread access to electric assist bikes (also called pedelecs) that allow people to go a bit further and reduce the deterrence of hills.
The PCT calculations for these scenarios start with flows between two areas (home and work). For each flow we assume people are equally likely to switch to cycling from any mode. Thus if for a given flow if most people currently drive then most of the cycling will come from driving, if they walk it will come from walking. Some flows see bigger shifts to cycling than others depending on their length, hilliness, and how many people are cycling already.
Generally, a policy goal has been to get people out of their cars and onto bikes, rather than from public transport or walking, because of the likely greater health and environmental benefits. In the PCT we only calculate carbon benefits for trips switched from cars (as we assume public transport will continue to run anyway) and if a trip is switched from walking to cycling that means less not more physical activity. However, cycling can still provide benefits in terms of journey time savings and increased accessibility to people who rely on public transport and walking. This can be seen in the Impacts of Cycling Tool www.pct.bike/ict, where the time savings benefits from cycling uptake are greatest for ethnic minority women, whose trips initially tend to be slower than those made by men and by white people.
However, with the Covid-19 pandemic people are being encouraged to walk, cycle, and in some cases drive rather than use public transport. Clearly where possible, it’s much better if people can shift to walking or cycling, and DfT has made available funds and statutory guidance to help local authorities provide extra space for walking and cycling. Some local authorities have created emergency transport plans and/or put in temporary walking and cycling infrastructure. A new Rapid Cycleway Prioritisation Tool, which builds on data from the PCT project, is being used to help identify promising roads for road space reallocation schemes.
As the PCT team we want to support efforts to overcome constraints on public transport supply and prevent gridlock. So we have created new area level datasets, showing the cycling potential (using the ebikes scenario) just for those trips that come from public transport. The data are available as spreadsheets at the local authority level and at the lower layer super output area level.
These data show the number of new cyclists in the standard Ebikes scenario, and splits this up to show how many of those new cyclists come specifically from buses and train/tube. They also include the health and health economic benefits specifically from those shifts away from public transport (including extra years of life and reduced sickness absence, due to increased physical activity). Carbon benefits are not calculated for trips from public transport.
The existing PCT flow level downloads can also be used in conjunction with these area level estimates. These show the number of new cycling trips and if the previous mode was public transport, walking, or driving but we have not calculated the specific health benefits of the ex-public transport trips at the flow level.
Of course, there remain good public health and environmental reasons to reduce car travel and to support public transport, and arguments that well managed public transport is still safe during the pandemic. However, we hope these data can help authorities focus their efforts to stop a shift to the car as people return to work, and to realise the large health gains that could come if some trips instead shifted to cycling.
We recognise that many people are still working from home and this will vary a lot between areas. Therefore, this data should be considered where possible alongside local estimates around the scale of home working among former bus and train commuters. Given the nature of the occupations who can work from home, bus commuters are probably less likely to be able to work from home.
In the London boroughs but also in Liverpool, Gateshead, Birmingham, Wirral and Nottingham, the problem is most acute because previously the most people are using public transport. However, correspondingly the potential health benefits of shifting them to cycling are also greater. For example in Lambeth there would be 7 fewer deaths and 23,000 fewer days of sickness absence per year in our Ebike scenario among former public transport users. Nationally the figure would be nearly 200 fewer deaths and 900,000 fewer days of sickness absence.