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Key Takeaways from The Power of Data for Social Inclusion Masterclass

1st May 2024

Discover what data is available for assessing transport needs, what to do when you don’t have the data you want, and the links between transport and social deprivation.

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In 2023, CIHT published a report on the role of data and artificial intelligence in achieving transport decarbonisation.

Through this work, we highlighted that good quality data collection is key to creating successful digital tools, including but not limited to, those that use artificial intelligence. One of the recommendations to come out of this report was that ‘the highways and transportation sector must harness the power of data’.

CIHT believes that there needs to be a greater consideration of not just the role data plays in supporting artificial intelligence but also how it can be used to enhance the experience of all transport users.

We held a CIHT Masterclass on The Power of Data for Social Inclusion on 21 March 2023 to showcase two recent projects which have used data to highlight transport inequalities and what they have done to improve this.

Below are some key highlights from presentations by Gideon Salutin, a transport policy researcher at the Social Market Foundation on ‘A New Metric for Transport Poverty’ and Amy Pidwill Senior Road Safety Lead and Raphael Canty, Principal Data Analyst from Transport for London (TfL) on ‘Inequalities in road danger in London’.

Types of data available when assessing transport needs

In the two presentations, we heard about many data sources that were used to help inform the projects when it came to transport related social deprivation.
These data sources were:

Index of Multiple Deprivation - small area measures of relative deprivation across each of the constituent nations of the United Kingdom. Areas are ranked from the most deprived area (rank 1) to the least deprived area (rank 10).
Income deciles – a ranking of equivalized household income in the UK, divided into ten equally sixed groups from lowest income (rank 1) to highest income (rank 10).
National Poverty Line - households are considered to be below the UK poverty line if their income is below 60% of the median
Mode preference - data about people travelling by mode of transport, produced by Department for Transport.
London Travel Demand Survey - each year, Transport for London randomly select a sample of 8,000 households in London to be interviewed about their travel habits.
STATS19 – data on collisions (that occurred on a highway, involved one or more vehicles and human death or personal injury) which are reported/recorded by the Police.
Census data – survey held every 10 years to provide a picture of all the people and households. Undertaken by the Office for National Statistics (England and Wales), National Records of Scotland (Scotland) and Northern Ireland Statistics and Research Agency (Northern Ireland).

Knowing the limits of your data

During the Q&A session, the issue of a lack of active travel data was raised, and it was suggested that this be solved by harnessing data from apps like Strava or, could we create an active travel equivalent of the UK Biobank? (Which collects voluntary genetic, lifestyle and health information and biological samples from half a million UK participants).

However, Amy Pidwell and Gideon Salutin both commented on the unreliability of volunteered data.

There is a risk that data sets which ask people to opt in massively skew in one direction.
According to Strava, in London the place where you get most cycling is Richmond Park…people going around and around trying to beat their personal best. We’re not going to be choosing that data to plan our transport network.
Observational data gives us a broader, more representative picture than volunteered data, but ultimately it is in an imperfect system and you need to blend datasets. That way you use networks, sensors and surveys, taking ‘a bit of this’ and ‘a bit of that’ to build up a picture, that is never going to be entirely and perfectly true – but it is going to be your best and most accurate representation. - Amy

When confronting data limitations, sometimes we eventually have to accept we can’t do this, and that’s okay. It is better to not say anything that you are doubtful about. But it’s also okay to not have the data on everything for every policy intervention, and when that is that case you can use other methods such as looking at global policies to see what has worked well in some places and not in others. - Gideon

8% of households are in transport poverty

Gideon Salutin presented on a recent publication from the Social Market Foundation (SMF) ‘Getting the measure of transport poverty: Understanding and responding to the UK’s hidden crisis’.

Gideon’s presentation highlighted that 8% of households, or 5 million individuals, are in transport poverty (when the total costs of private and public transport drive a household into poverty), with the highest rates of transport poverty being seen in the North of England and the West Midlands. Transport for the North have also done research into this issue in their report 'Transport Related Social Exclusion in the North', which revealed 3m people in north of England ‘face social exclusion due to poor transport’.

The research done by SMF also showed that freezing fuel duty does little to alleviate transport poverty. Fuel duty freezes since 2011 have only saved the median UK household £13 per month, decreasing poverty by just 0.3 percentage points, despite a cost of £100 billion to the Exchequer.
To decrease the number of people in transport poverty, the SMF has made several recommendations, including:

Recognise that freezes to fuel duty have failed to decrease transport poverty and allow the rate of fuel duty to rise or replace it with road pricing mechanisms.
Use transport poverty metrics to target extensions of public transport networks outside London where they would be most effective at reducing transport poverty.

Collisions in London are linked to deprivation

Amy Pidwill and Rapheal Canty presented on TfL’s ‘Inequalities in road danger in London (2017-2021)’ report which highlights that:

Twice as many casualties occur on roads in the most deprived parts of London than in the least deprived (per km).
Twice as many collisions occur involving people living in the most deprived parts of London than the least deprived (per 1,000 people).
Over twice as many men are killed or seriously injured vs women in the most deprived parts of London (per 1,000 people).

TfL have made the data behind the ‘Inequalities in Road Danger in London’ report readily available in their new dashboard tool which allows users to gain insight into deprivation and casualty home postcode and collision location.
TfL plan to develop the dashboard to include more characteristics such as age and sex, which should be ready by September 2024.