Innovate UK, the UK’s innovation agency, awarded funding to Arup as part of their ‘Business-led innovation response to global disruption’ competition in the immediate aftermath of the UK’s lockdown. In just six weeks, we developed the foundations of an Agent Based Model (ABM), which simulated individual behaviours and travel changes when regular behaviours were disrupted by restrictions such as social distancing.  

This model formed the basis of future analysis at the granular level of individual behaviour. Working in collaboration with Birmingham City Council and Transport for the West Midlands, we built an ABM for the Birmingham region to test our approach.

The project required us to think differently. Testing radically different behaviours for which there was no precedent required a radically new approach. Our aim was to demonstrate how advanced machine learning can be applied to reduce cost and increase the speed of planning for transportation services in uncertain environments.

Providing value faster in the face of time-critical challenges

The COVID-19 pandemic presented new challenges for traditional models. These models can take months or years to build and simulate behaviours, and they can struggle to represent behaviour that looks different from the past. Our challenge was to develop the foundations of a model that could provide useful insight into radical shifts in behaviour. We needed to work fast to build the foundations of a model within weeks that would help transport agencies plan for short- and medium-term consequences of changes in travel behaviours because of the pandemic.

The key to ABMs is that they simulate behaviours at an individual level. They take into consideration different characteristics, needs, and resources, and provide a unique opportunity to reflect changes in behaviour. In contrast to traditional modelling, this granular approach gives planners the ability to consider futures that are radically different from the past and allows us to model a wide range of impacts with greater flexibility.

The multimodal transport network in Birmingham representing both major routes and smaller, urban roads.
The multimodal transport network in Birmingham representing both major routes and smaller, urban roads.

Modelling changing behaviours

As the UK’s second largest city, Birmingham presented an ideal location and opportunity to test our ABM approach. Working in collaboration with Birmingham City Council and Transport for the West Midlands to understand their data and priorities for decision making during lockdown, we built a multimodal network of the West Midlands region. Using a combination of geographical, timetable and census data, we were able to build a baseline model to reflect transport behaviours pre-pandemic. The model was then used to run simulations to represent impacts of the pandemic and provided visualisations of the outputs.

From concept to delivery in six weeks

Time was a critical consideration from the outset. With behaviours changing overnight as a result of new restrictions, we needed to be able to develop an ABM that simulated behaviours quickly and accurately. In the wake of the pandemic, our data scientists developed the Pandemic Activity Modifier (PAM). This open-source pre-processing software alters the behaviour plans of agents based on the introduction of new government policies and allowed us to automate elements of our ABM to expedite its delivery.

In just six weeks, we developed a model that simulated approximately 200,000 individual agents - 10% of Birmingham’s population. The represented changes in travel behaviours compared favourably against our benchmark data, showing agents changing behaviour and shifting from public transport to private cars and allowed for more informed and knowledgeable conversations around the modelled scenario results. Using a traditional model, this would not have been possible.

The future roadmap

As a business that is forever looking to the future, in parallel to the build of the ABM, we set out a roadmap for how the model could be enhanced. This included understanding how and which communities might be most impacted by any changes to public transport services, and these developments will help to answer specific questions around both the pandemic and transport interventions in general in order to support successful transport planning in our cities.