  Truck transportation remains the dominant mode of US freight transportation
because of its advantages, such as the flexibility of accessing pickup and
drop-off points and faster delivery. Because of the massive freight volume
transported by trucks, understanding the effects of population and employment
characteristics on truck flows is critical for better transportation planning
and investment decisions. The US Federal Highway Administration published a
truck travel origin-destination data set as part of the Next Generation
National Household Travel Survey program. This data set contains the total
number of truck trips in 2020 within and between 583 predefined zones
encompassing metropolitan and nonmetropolitan statistical areas within each
state and Washington, DC. In this study, origin-destination-level truck trip
flow data was augmented to include zone-level population and employment
characteristics from the US Census Bureau. Census population and County
Business Patterns data were included. The final data set was used to train a
machine learning algorithm-based model, Extreme Gradient Boosting (XGBoost),
where the target variable is the number of total truck trips. Shapley Additive
ExPlanation (SHAP) was adopted to explain the model results. Results showed
that the distance between the zones was the most important variable and had a
nonlinear relationship with truck flows.
