Understanding how we move around our towns, cities and regions is a complex thing. For around forty years we’ve been attempting to model traffic and transport in order to better provide for it, and last week I went along to the University of Westminster to see how modellers were getting to grips with cycling.
Transport modelling at the level of a city or region has, for a long time, relied on simple cost-time calculations. In short, they estimate how many people are travelling, where they’re traveling from and to, and what route options they have - largely assuming that we travel the quickest and cheapest way.
The model then produces figures for the ’demands’ placed on a road network from bus and car use – and if you’re lucky cycling. But there are a number of sticking points when it comes to modelling for cycling.
As much as transport planners, practitioners and campaigners would like to see modelling incorporated into their toolkit for transformative change, the limitations remain all too strong – at least when we talk of the more ‘traditional’ models.
First, these models are only as good as the data it’s based on and, for a couple of reasons, there’s a distinct lack of data on cycling. Primarily because cycling’s share of travel is low it’s difficult to capture with any accuracy the way someone who cycles gets about, what routes they value and what type of trips they’re making. Additionally, there’s been no systematic approach to monitoring cycling.
I would suggest these two problems are due to the same fact: cycling’s never really been a ‘national’ priority so it’s never really been promoted, built for or encouraged, and likewise it’s never systematically been monitored in sufficient detail.
The second challenge to building a model with cycling in mind was that any data we do collect now, could be markedly different to the types of cycling we might hope to create. This is especially true for London, where the Mayor hopes to ‘de-lycrafy’ cycling and even then the London assembly members are rightly demonstrating the need for more ambition in doing so.
The better the conditions for cycling, the less the need for fast bikes, sweat defying lycra and prime positions. So what implications will that have for understanding and modelling for a ‘cyclists’ behaviour, when there are a many different yet to be ‘cyclists’ out there?
And so we find large scale modelling in a dilemma. The data from which we project forward is of the car-dependant infrastructure and culture we need to move away from. What’s more the assumptions underpinning time-cost models are being questioned by sharp increases in cycling, particularly in London and on specific routes - unaccounted for in earlier predictions.
As much as strategic models project future demand they remain steadfastly ‘business-as-usual’, projecting forward ‘demand’ without reflecting on the role of the model and in turn decision-makers on creating that demand.
Only with strong political leadership and a bold cycling constituency are we beginning to see public agencies and consultants – those closest to the decision making process –explore cycling. Where the pressure is on to better provide for cycling – London, Cambridge and Wales - the large-scale modellers are making tentative steps forward.
The balance of trust needs to shift to cycle experts and local knowledge. More temporary measures allow us to see if something works, make it permanent or force a rethink. Decision-makers need to step out from behind models and begin to realise their agency in shaping our everyday travel.