Forecasting Commercial Vehicle Miles Traveled (VMT) in Urban California Areas

This study investigates commercial truck vehicle miles traveled (VMT) across six diverse California counties from 2000 to 2020. The counties—Imperial, Los Angeles, Riverside, San Bernardino, San Diego, and San Francisco—represent a broad spectrum of California’s demographics, economies, and landscapes. Using a rich dataset spanning demographics, economics, and pollution variables, we aim to understand the factors influencing commercial VMT. We first visually represent the geographic distribution of the counties, highlighting their unique characteristics. Linear regression models, particularly the least absolute shrinkage and selection operator (LASSO) and elastic net regressions are employed to identify key predictors of total commercial VMT. LASSO regression emphasizes feature selection, revealing vehicle population and fuel consumption as significant predictors in most counties. Elastic net regression, which balances feature selection and multicollinearity, expands the list of predictors to include variables like the number of trips, CO2 emissions, and PM2.5 pollution. Overall, the findings suggest that economic factors, such as fuel consumption and vehicle population, significantly impact the total commercial VMT across the counties. Pollution variables, specifically CO2 and PM2.5, also play a role. These insights underscore the need for nuanced transportation and environmental policies, especially in the face of economic fluctuations, to manage commercial truck VMT effectively and sustainably. Methodology using both LASSO and elastic net regression provides a robust framework for understanding these complex relationships in commercial transportation behavior.