CASA: A MATSim-Based Platform for Investigating Methods to Reduce Traffic Emissions

Finland has some of the cleanest air in the world yet still suffers from approximately 2 000 pollution related, premature deaths a year. In Helsinki, traffic is the most significant local contributor to the concentrations of fine particles (PM 2.5) that are the most detrimental to human health, and produces 20 percent of the city’s CO2 emissions. Reducing the adverse health effects of particles and cutting CO2 emissions require a change of behaviour on individual level, as well as structural changes on a city-wide scale. Using MATSim, agent-adaptivity, atmospheric modelling and AI methods on various scales, we present a platform to test and create incentive-based policies that encourage populations of urban centres to take more climate sensible transport modes, with quantifiable impacts on emissions and atmospheric composition.