Abstract
This project investigates air pollution in California communities disproportionately affected by their proximity to transportation corridors, industrial facilities, and logistics centers, focusing on truck-related activities, including idling, parking search, and parking demand, using comprehensive datasets and robust models employing techniques such as Random Forest, Convolutional Neural Network, Bayesian Ridge Regression, and Spatial Error Model. Key findings reveal factors affecting idling times, parking search times, and parking demand, with heavy-duty trucks having the highest idle times and parking search challenges concentrated around transportation arteries and freight yards. The Spatial Error Model highlights relationships between truck activities, socio-economic variables, and air pollution in AB 617 communities. Based on these findings, preliminary policy recommendations include targeted anti-idling campaigns, improved truck parking facilities, cleaner fuels and technologies, enhanced routing efficiency, stricter emission standards, and strengthened land-use planning.
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