BACKGROUND AND AIM[|]Traffic-Related Air Pollution (TrAP) adversely impacts human health, disproportionately harming disadvantaged communities. New technologies and infrastructure offer opportunities to reduce TrAP, but the health outcomes of individuals are not fully understood due to a lack of high-resolution models that grasp the complexities of transportation systems and their health implications amid evolving policies and technologies.[¤]METHOD[|]We introduce BEAM CORE (beam.lbl.gov), a high-resolution, agent-based transportation framework that simulates detailed passenger and freight activities. It captures interactions between transportation, land use, demographic and vehicle ownership changes at various scales. Validating crucial factors of emission modeling, including link-level VMT, speed and regional fleet in the San Francisco Bay Area’s nine counties, demonstrates its potential to be extended for assessing health outcomes from changes in TrAP.[¤]RESULTS[|]All major outputs from the BEAM CORE 2018 baseline have been calibrated and validated. Mode split and demographics align closely with census and survey data. Passenger and freight activities were validated against public and private data, with CO2 emissions corresponding to 3.67Mt/yr for medium/heavy-duty (MHD) and 22.79Mt/yr for all vehicles, demonstrating the model’s alignment with empirical data. The NOx, PM2.5 and PM10 from MHD exhaust, PM brake and tire wear are 14.8kt/yr, 424t/yr and 606.9t/yr under the 2018 baseline with high fractions of conventional vehicles, while the wide adoption of clean truck technologies under 2050 resulted in 87\%, 75\% and 56\% reductions respectively. BEAM CORE generates detailed fleet and activity data at high spatiotemporal resolution, enabling the integration with air quality models, including InMAP/AERMOD, to explore the causal pathway of health impacts from transport policy changes.[¤]CONCLUSIONS[|]We developed a sophisticated multi-dimensional transportation model for integration with advanced air quality, and health assessment models. It enables a thorough analysis of health impacts of transportation policies and technologies across diverse communities. It supports similar analyses in any area using local data.[¤]