Discovering environmental health effects of transport scenarios through agent-based simulations

Abstract

Urban planning can help tackle environmental health issues. We demonstrate that agent-based simulation can discover unintended as well as intended environmental health effects and social inequalities of intervention scenarios. We developed, calibrated and validated UrbHealth-ABM, an empirically grounded agent-based model of Amsterdam, the Netherlands, integrating data and models of individual mobility choices, traffic, air pollution, physical activity and personal exposure. We used the 2019 parking price increase as a natural experiment, confirming the models’ accuracy in predicting traffic reduction. Projections for the planned 2030 no emission zone show a significant reduction of nitrogen dioxide exposure for everyone and an increase in transport-related physical activity, especially for less affluent outer-city residents. However, disproportionate increases in their travel times raise equity concerns. Several 15-minutes city scenarios reveal that although driving may increase to distant destinations, nitrogen dioxide exposure decreases overall. Moreover, transport-related physical activity might decrease in the Amsterdam context due to shorter active travel distances, but travel time savings could be used for mitigation strategies