On 26th June, Social Statistics Section held a meeting about Investment for Active Travel, using substantial example from London. Nico Jabin, head of local evaluation at the Department for Transport chaired the meeting. He began by explaining that randomised trials were impractical and therefore transport was more interesting from a social research perspective.
Two presentations from Transport for London (TfL) showed how big data really was shifting city planning capability. Rob Forrest presented the modelling of cycling ‘cynemon’ (Cycling Network Model for London - which estimates cyclist routes, journey times and flows at a strategic level), which was developed and validated using large amounts of different data, including more than 2,000 automated cycle counters and demographic projections from ONS. This model allowed understanding of trends such as preference for major roads, and simulation of the impact of new layouts. Phil Howes presented modelling of walking, for which there is much less data available. But there is also commensurately less use of data in planning so the modelling has developed a new hexagonal geography, and can recommend where investment may be needed.
Rachel Aldred of University of Westminster then presented results from the first year of (TfL-funded) evaluation of new investments in three London boroughs. The mini-Holland scheme has transformed substantial parts of the streetscape in these boroughs to give pedestrian priority and restrict car movements. The three boroughs had different responses, with slower investment in one area and seeing commensurately less change. A statistically significant change (difference in differences) was seen in more developed areas, specifically in the parts where investment was intense. This was explained as a response of latent demand being unleashed rather than behaviour change, however. The commitment to follow this up in the longer term means some of these theories can be tested.
Further discussion included topics of funding and political design choices, as well as further aspects of behaviour change. There is now potential for other authorities to use some of the findings and technology from the modelling, even if they cannot match the amount of data collected. Similarly there may be a lot of data on flows of people, this needs to be combined with behavioural data about their journey purpose, choices and attitudes.