Propensity to Cycle, and the importance of main roads

The National Propensity to Cycle Tool (PCT) is a powerful planning tool which shows existing commuting cycling trips (based on mapping the 2011 census), and then uses that data to illustrate where the main cycle flows are, or should be, and therefore where cycling infrastructure should be prioritised.

Importantly, it doesn’t just cover existing cycling flows; it can be updated to show what (commuter) cycling levels would be like if we had the same propensity to cycle as Dutch people (adjusted for hilliness), and where people would choose to cycle, based on directness.

The purpose of the route allocation is to see on which routes the most provision might be necessary as cycling grows rather than to show where people currently cycle. We recognise that many people currently choose longer routes to avoid busy roads. But for cycling to reach its potential safe direct routes are needed. The Route Network layer is therefore intended to show where (on which routes) investment is most needed rather than where people currently cycle.

I’ve been playing around with it over the last few days, based on the town of Horsham, and the results are quite instructive. Based on the census results, cycling to work levels are currently a fairly miserable 3% of all trips to work in the town centre.

I say ‘miserable’ because the town is flat and compact – only around 3 miles across, and with all trips (even from some surrounding villages) less than 2 miles from the centre.

Despite this favourable geography, the town (population around 60,000) is dominated by car commuting – between 40 and 50% of trips to work are driven.

The Propensity to Cycle Tool is great because it allows us to visualise alternative scenarios, and how to prioritise designing for them. We can plot the cycling trips currently being made from area to area (in the 2011 census) as straight lines.

Then (and here’s the clever bit) we can see how those trips would be made by the most direct routes, mapped onto the road network.

The levels of cycling can then be changed by shifting from the 2011 census to either the (unambitious) government target of doubling cycling levels, ‘gender equality’, ‘Go Dutch’, or ‘Ebikes’.

What is really interesting (but unsurprising) is that the routes being taken don’t change as the levels of cycling increase, as you can see from the ‘Go Dutch’ scenario shown above. It’s unsurprising, of course, because people will still choose the direct routes, regardless of whether they happen to be part of a small number of cycling commuters, or part of a town with mass cycling. Why would they change to less direct routes?

The great thing about this tool is that it shows exactly where interventions should be prioritised. I can see clearly from the map above that two of the most important routes (at least for commuting) in Horsham are the two roads north of the town centre – North Parade, and North Street.

It just so happens that these are two roads where there is plenty of space to incorporate high-quality cycling infrastructure, with only the loss of some grass, and central hatching – and the existing, poor, cycle lanes.

North Street

North Parade

So if, for instance, we were looking to prioritise where to invest in cycling infrastructure for the most benefit (rather than just looking to do tokenistic improvements ) these two roads would be among the main priorities. The PCT tool even allows you to click on the roads in question, to bring up helpful information. For instance, ‘Going Dutch’ would mean taking nearly 200 car commuters off this particular road.

If it wasn’t already clear, main roads are quite obviously where interventions are required, and where they will be most useful. They are main roads for a reason; they tend to form the most direct routes, and they also connect between the places people are coming from, and going to. The Propensity to Cycle Tool isn’t really showing the equivalent of back street, or ‘Quietway’, routes. The cycle flows are all on the major roads, or on the distributor roads that connect up residential streets.

What will make this tool really powerful is when it is released in ‘Version 2’ next year, because it will incorporate other journeys, not just commuting – because obviously only a minority of the trips we make are actually trips to and from work.

Version 2 will go beyond commuting data to incorporate other trip purposes, including education trips at route and area level and other non-commuting trips at area level.

Apparently commuting flows are actually a good approximation for travel flows in general, but incorporating trips for education, leisure and shopping will make the case for cycling even more powerful.