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.