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City Modelling Lab

Improving transport planning by simulating everyone’s day

Improving transport planning by simulating everyone’s day

The motivations we each have for the different journeys we make and our decisions around the transport we use are complex and varied. Scale that up to consider entire urban populations, and the challenge facing transport and planning authorities become apparent. They are trying to formulate policy, anticipate demand on their travel networks and define investment. They are having to reconcile these decisions with improved social welfare through access to education, healthcare and jobs. All whilst striving to achieve net zero.

Our City Modelling Lab brings together our experts in transport, energy, climate change and economics with data scientists, software engineers and designers to answer these challenges. We build models at city, regional and national levels.

We’re using machine learning to put the human back at the centre of city planning. ” Claire Fram Claire Fram Senior Product Manager

Find out more about our work with machine learning

In modelling future demand, we do not build one plan. We run multiple simulations to create a spectrum of scenarios. Scenarios that cater for uncertainties and highlight where the sensitivities lie. Multiple scenarios that help authorities to be more imaginative about what a better future may look like. The rigour that informs our models allows authorities to plan those future policies and investments with new levels of confidence and clarity.

City modelling lab agent based model questions and scenarios City modelling lab agent based model questions and scenarios

Agent Based Modelling (ABM) captures the variety and nuance of the individual decisions that guide our travel choices. By modelling individuals, we can replace generalisations about air quality, for example, with precise individual exposure levels.

Agent based modelling

Traditional modelling fails to mirror the complexities of life and the rapidly changing transport sector. Assumptions on how people move and behave have grown increasingly inaccurate over in the past decade as choices have expanded in number and complexity.

The ambition for our City Modelling Lab is simple. To simulate everyone’s day more accurately and rapidly than previously possible.

We capture that individuality by using Agent Based Modelling (ABM). We model each individual, each with a plan, each making decisions on where to travel, when to leave and what mode of transport to use. Each influenced by the choices of other individuals in the model and the evolving choices available to them.

Unlike traditional simulation, we model the ‘why’ as well as the ‘what’, gaining insights into complex behaviours. This allows us to better understand shifts in travel behaviour, establishing a truer perspective on the impacts of policy decisions, city planning and new transport schemes enabling more fair, equitable and sustainable decisions and investments for our society’s future.

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Understanding people’s evolving priorities at different times of the day at a large scale can support faster and more accurate decision making to help build a sustainable future. ” Gerry Casey Dr Gerry Casey Technical Lead

Featured project

Helping Ireland prepare for future road users

As Transport Infrastructure Ireland builds its policies on road tolls and reducing emissions, our modelling is helping to them interrogate the consequences and create a fair, progressive pricing model.

Find out more about our work

Modelling city life at scale

Cloud computing has unlocked the ability to model the complexities of city life at massive scale. As Transport for London’s ABM research partner, our model of the city uses over 1,000,000 agents each making 750 decisions over the course of their day. The acceleration in computing power now allows these high-fidelity models to run in the same timescales as less refined, aggregate models. It also opens the way for greater equity in transport planning, reflecting the needs of a wider range of people.

We build our models ground up, using travel diaries and census details to populate networks with individual profiles. Faster computing will also allow us to add more data sets and increase the accuracy. We are exploring how to overlay mobile phone and credit card data to create richer profiles, modelling individual behaviour at the micro level.

Featured project

Simulating changing transport behaviours

From journeys to work, for education and shopping, the Covid-19 pandemic radically transformed travel. We were awarded a grant from Innovate UK to build an alpha Agent Based Model (ABM) of Birmingham and the West Midlands, testing authorities’ ability to respond quickly and accurately to major changes in travel behaviour.

Find out more about our work

Transparent thinking

Decisions around transport infrastructure and land use attract a great deal of public scrutiny. We have built our City Modelling Lab using open source software. We use open source projects, drawing on the technical expertise of the community, and publish the open source software we develop. This allows public authorities to remain transparent and accountable. Stakeholders can view and interrogate the data that informs the decisions that will affect their lives.

Transport planning operates with long time horizons. The Lab’s open source foundations free authorities from a single proprietary software. For New Zealand’s Ministry of Transport, we are not only building a national-scale model for the next 50 years. We are building the Ministry’s own ability to create, run and model future scenarios.

(c) Paul Dingman (c) Paul Dingman