Data Counts
In the era of big data, smart tech and ubiquitous CCTV there is more and more data available about every aspect of our lives. What we watch, what we buy, what we eat, where we go, how many steps we take, how much we sleep and on and on and on. Computers, smartphones, wearable tech, sat nav, CCTV and more — our digital footprints are large and growing.
I’ve been trying to map out the landscape of transport data as part of some research I am working on — specifically trying to work out why the data seems to be good at answering some questions, and terrible at answering others. As Caroline Criado-Perez has pointed out time after time, data can hide as much as it reveals — particularly in relation to the lives, experiences and needs of women and girls.
Here’s what I have learned about the ‘official’ statistics collected by the government and its agencies. I’ll write about other sources of travel data another time…
National level official statistics from the Department for Transport (England) and Transport Scotland (Scotland) can be grouped into two types:
- data about traffic flows, vehicle movements and vehicle types focussing on the things that are moving (cars, vans, lorries) or being moved (passengers, freight)
- data about travel behaviours —focussing on people and how often they travel, the purpose, length and mode of their trips.
Both types of dataset are updated regularly — often on an annual basis, and there are some good time series data to show trends over time. And, overall there is a lot of data available. An impressive amount. So far so good.
Looking further into the data that’s available, it seems astonishing that in this digital age most national traffic flows and forecasts are based on manual counts rather than continuous monitoring from automatic traffic counters (ATCs). In England there are around 10,000 manual counts and 180 ATCs feeding into road traffic flow data — with 24 ATCs and 450 manual counts in Scotland. ATCs provide a continuous flow of data 24 hours a day and provide information about vehicle type — so they are a really useful source of data, but there are staggeringly few of them in the UK. In contrast, manual count data covers only 12 hours out of 24 and are carried out on neutral days, e.g. a normal weekday between March and October not affected by a holiday or special event. So, they only capture the most average of average days in the year, and give no insights into seasonal changes, the effects of holiday traffic or information about night time traffic flows. Transport Scotland helpfully notes that traffic estimates indicate a broad level of traffic and should be treated cautiously — particularly for year on year comparisons — because of the limited number of sites sampled across the network.
Social survey data about travel habits comes from annual surveys such as the National Travel Survey (NTS), Scottish Household Survey (SHS) and Active Lives Survey (ALS) plus intermittent sources like the Census. These datasets tell us about household access to cars and bikes, and the types of journey people make — including travel to work, business travel, education, leisure or shopping. This gives us insights into overall travel patterns and some of the variations between groups in the population, like full-time and part-time workers, older people (>65), men and women. The NTS and SHS both include a travel diary, capturing the movements and journeys of participants over a specific week. The NTS travel diary is completed by 15,000 participants over a one week period each year, whereas the travel diary component of the Scottish Household Survey only covers one day — the day before the survey takes place — and is completed by a random adult in each participating household. The sample size for the SHS is about 10,500.
For a researcher like me, interested in active travel behaviour among women, children and other minority groups in the population, this social survey data is a useful starting point. It gives us insights into the travel behaviour of different groups (e.g. men and women) — although we have to bear in mind that a lot of the data that is available is based on samples, thousands of data points rather than millions — and none of the sources mentioned above cover children. At the same time another key limitation is that none of this data captures the actual geography of the journeys made, e.g. recording the locations of the journeys or the routes taken. Only the Census includes questions about journey origin and destination, covering travel for work and education but not any other purposes (e.g. leisure or caring) — although this is only collected once every ten years.
In sum, there is certainly lots of data available at national level about traffic and transport. In some ways this data is very rich and comprehensive, and in other ways it’s very limited. National level flow data about vehicles, passengers and freight is collected more regularly than social survey data about travel behaviours — and a lot of datasets are based on samples, providing a snapshot of travel behaviour over a very short time period (one day or one week) or for a limited time period on a neutral week day between March and October.
From my view point, I’m interested in the links between data about traffic/passenger flows and data about who travels and where — because none of those lorries are driving themselves (well, not yet anyway) and there are reasons behind every journey that are invisible to the sensors that capture vehicle speed and weight (a delivery of goods to a store warehouse; an annual MOT/service; commuting to work; an urgent trip to care for a family member). It seems — from my review — the national data is pretty limited for anyone trying to understand why people walk or cycle (or not) in a particular place — although it is useful as a benchmark for understanding overall patterns of cycling and walking and how these are changing over time. But the links between the who is travelling, why and where seem important to me — and I just see gaping hole after hole in all that object-based data about traffic and passenger flows.
One piece of information that constantly blows my mind when I think about it, is this: 99% of HGV licenses in the UK are held by men. That’s astonishing and revealing in itself. But, if we added that information to data about traffic flows, then maybe we would notice
almost every lorry on our roads is driven by a man.
Makes me wonder what else we might notice if we could add details about who is travelling to all that data about how much travelling is happening… .
