The MATSIM Pandemic Model for Greater London – Gerry Casey, Claire Fram and Fred Shone

Interaction based risk - an agent based analysis of London   We have seen unprecedented changes in transport demand and supply, as a result of Covid-19. Transportation can play a key role in the regional economic recovery by enabling people to safely access work, healthcare, education. But we know that Covid-19 is highly contagious, and there are no current vaccines. Transport agencies will need to plan safe services that minimise crowding, maintain critical service schedules, and mitigate new challenges as operating resources are pinched.   Established transportation modelling techniques are not designed to analyse how individuals travel, at a granular level. This makes it challenging to estimate how changes in transport demand (the result of physical distancing policies) may impact public transport and identify where coronavirus transmission risk may be greatest. Agent based models (ABMs) provide granular insight into how individuals behave. The aggregate consequences of individual behaviours can be analysed to understand where and when congestion may happen, which populations make use of which public transit services, and other network-wide consequences.   Arup has been working with TfL to understand how public transport may impact the risk of Covid-19 transmission in London. We have extensive experience in developing ABMs – including an ABM for London, developed as part of a previous, collaborative TfL-Arup research initiative. We have been testing a new epidemiological model in conjunction with our London mobility ABM that attempts to take into account "virus infection dynamics" (EpiSim , developed by Technische Universität Berlin).