Abstract Summary
With ever more people living in cities worldwide, it becomes increasingly important to understand the positive and negative impacts of the urban habitat on livability, health behaviors and health outcomes. However, implementing interventions that tackle the exposome in complex urban systems can be costly and have long-term, sometimes unforeseen and indirect, impacts. Hence, it is crucial to not only assess the health impact of interventions, but also its cost-effectiveness and the social distributional impacts of possible urban exposome interventions before implementing them. Spatial agent-based modeling can capture complex behavior-environment interactions, exposure dynamics, and social outcomes in a spatial context. We present our agent-based model of transport interventions in Amsterdam. Our approach entails the integration of a behavioral model of people’s mobility choices and dynamic physical models of environmental stressors (so far NO2). Together these sub-models result in an exposure interaction that approximates personal behavioral and environmental exposure for different population groups within an urban environment, e.g. based on demographics, the neighbourhoods they live in or on their social economic circumstances. We use our ABM to capture and compare the exposure and health impacts of multiple hypothetical transport intervention scenarios, such as pedestrian infrastructure improvements, speed limits and the 15min city. We find among other things that in Amsterdam the elasticity of mode of transport choice is larger in the afternoon hours, that low income groups benefit more from pedestrian infrastructure and that additional pedestrian infrastructure has a larger benefit compared to biking infrastructure. We present our model architecture, the strength and limitations of the method, and our preliminary findings on effects of transport interventions.
