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UK scientists use AI to build passenger transport simulators
Project involves using a set of 12 transport user personas to make a ‘synthetic’ population and encourage the use of public transit and active travel options.
At a glance
Who: TransiT; Heriot-Watt University; Department for Transport (DfT).
What: UK researchers at Heriot-Watt University are using AI to help build passenger transport simulators that resemble human behaviour to accelerate decarbonisation.
Why: To encourage car users to use buses, bikes and other active travel choices like walking.
Where: The project is focused on the West Midlands.
UK researchers are using artificial intelligence (AI) to help build passenger transport simulators that resemble human behaviour to accelerate decarbonisation.
The work is part of a transport decarbonisation demonstration project in the West Midlands, where scientists are looking for ways to encourage car users to use buses, bikes and other active travel choices like walking.
Creating a ‘synthetic’ population
Researchers Jingjun Li and Shiqi Sun are using a set of 12 transport user personas developed by the UK government’s Department for Transport (DfT), and aim to make a ‘synthetic’ population of the West Midlands where three million individuals behave more like real people.
“In a real transport network, there are bus users, car drivers, cyclists and commuters travelling to or from their work or home,” said Dr Sun. “With large volumes of transport users like this in a simulated environment, we can test the likely impact of changes to reduce carbon emissions. For example, if we lower bus fares, which transport users are more likely to switch to buses?”
“The synthetic populations we use in our transport research are great for showing the impact of things like timetable or route changes. But they can’t represent the kind of granular richness in human behaviour”
Dr Sun and Dr Li are based at Heriot-Watt University in Edinburgh as researchers with TransiT, a national UK research hub focused on rapidly decarbonising transport using digital twins.
The research involves using AI to train machine learning algorithms – computer programmes that learn from data – to link the DfT’s realistic profiles of transport users to the large West Midlands synthetic population.
Synthetic populations are datasets used in computer simulations to mirror population attributes like age, income and location, without using sensitive personal data. While they are a critical component of future-looking research in sectors including healthcare, urban planning, finance, transport and retail, they struggle to represent the unpredictability and diversity of human behaviour.
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“The synthetic populations we use in our transport research are great for showing the impact of things like timetable or route changes,” said Dr Li. “But they can’t represent the kind of granular richness in human behaviour that would be most helpful to our research. For example, different attitudes towards travel, or preferences when making transport choices.”
To help bridge this gap, Dr Li had the idea of linking the individuals in TransiT’s synthetic population with the DfT’s transport user personas. These are 12 research-based, fictional characters representing different transport user groups, and are used widely to research transport mobility and behaviour.
Manually labelling a synthetic population in this way would be a hugely time-consuming and laborious task but Dr Sun has developed a way to automate and accelerate the process using large language models (LLMs). The model Dr Sun has developed also includes an active learning component, where the algorithm learns to improve its selection process while carrying out the task.
The team have coined the term Active LLM Fusion (Alf) – to describe this learning process.
“Every time we feed new data to our model, it improves its prediction and labelling process,” said Dr Sun. “This means we can accelerate the speed and scale of our research, as well as helping our computer simulation experts to better predict the behaviour of real transport users.”
Applications beyond transit
The researchers reckon their modelling tool can be used in different population simulation contexts and see potential applications for their work in other sectors beyond transport. For example healthcare, where scientists need to research different segments of the population.
TransiT is a collaboration of eight UK universities and almost 70 industry partners jointly led by Heriot-Watt University and the University of Glasgow. It is funded by the UK Research and Innovation Engineering and Physical Sciences Research Council (EPSRC), the main funding body for engineering and physical sciences research in the UK, and supported by the UK government’s Department for Transport.
TransiT is using digital twinning and related digital technologies to identify the lowest-cost, least-risky pathways to net zero emissions in UK transport across all modes – road, rail, air and maritime – for both passengers and freight.
Its West Midlands passenger transport demonstrator is one of five critical demonstrator projects involving interconnected road, rail, air and maritime systems across different regions of the UK.
Using data from rail, tram and bus services, public transport and car, cycle and taxi use, the twin will help policymakers and transport planners identify what interventions would be most effective in shifting travel behaviour towards public transport and active travel choices like walking and cycling. Industry partners working with TransiT in its West Midlands demonstrator include Transport for West Midlands, Midlands Connect, and the global engineering consultancy, WSP.
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