Big data bigger impact | Evidence based transport solutions

Recently, the ITP team responded to the DfT’s Integrated National Transport Strategy (INTS) ‘Call for Ideas’. This post is the first in a series of more detailed follow-ups from our experts on the topics raised.

Data is the evidence you need

As discussed in our response, traveller-facing information (real-time journeys, service updates, route planning, etc) is crucial for achieving more sustainable transport networks by improving experiences of public transport and active travel. But this post will explore the planning angle, where diagnosis, analysis and evidence-led decisions help to deliver lasting network improvements for the travelling public.

For thirty years, computers have been getting smaller, cheaper, and increasingly attached to all types of sensors and detection systems. ‘Big’ data was seen as the next big thing ten years ago – but the challenge of making use of improving technology remains. At ITP, we understand that data is evidence: a description of the real world, with numbers that relate to things that you need to know. But the biggest challenge can be understanding what your evidence is telling you.

Computer vision is the ‘quiet revolution’ taking place in machine learning, while the noisier GenAI is providing us all with inedible recipes and unfunny jokes. Having the ability to rapidly count cars, buses, and pedestrians at every point in a city, from pre-existing datasets, gives our international team the ability to develop a rapid understanding of our project locations, giving mode-share, neighborhood intelligence, and network speeds. This often uses data that already exists online – or can be captured cheaply using commodity tools such as Android smartphones.

Fare modelling from big survey data

Our final example of ‘data as evidence’ is recent work we’ve completed for a major city region in the north of the UK. Our client had a comprehensive survey dataset: hundreds of thousands of origin-destination datapoints for passengers using their network. Our work helped them to understand what changing fare boundaries would do to ticket revenue – and helped them play out different scenarios to target revenue neutrality and maximising public transport uptake. Our work also considered the distributional aspects of ticket pricing, ensuring that price changes did not further disadvantage the less affluent of the region. The resulting financial forecasting is not just evidence-based, but crucially, based on the biggest set of evidence available.

So what?

These three examples show different ways in which the ITP and Haskoning teams have used new and larger datasets to target our clients’ priorities in different ways. We understand the latest data sources that cover your local area or organisation’s operations, and can relate them to evidence that targets your priorities, whether it’s faster buses, improved active travel, or more broad economic growth.