  The US Census Bureau has collected two rounds of experimental data from the
Commodity Flow Survey, providing shipment-level characteristics of nationwide
commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017
(i.e., Public Use File). With this information, data-driven methods have become
increasingly valuable for understanding detailed patterns in freight logistics.
In this study, we used the 2017 Commodity Flow Survey Public Use File data set
to explore building a high-performance freight mode choice model, considering
three main improvements: (1) constructing local models for each separate
commodity/industry category; (2) extracting useful geographical features,
particularly the derived distance of each freight mode between
origin/destination zones; and (3) applying additional ensemble learning methods
such as stacking or voting to combine results from local and unified models for
improved performance. The proposed method achieved over 92% accuracy without
incorporating external information, an over 19% increase compared to directly
fitting Random Forests models over 10,000 samples. Furthermore, SHAP (Shapely
Additive Explanations) values were computed to explain the outputs and major
patterns obtained from the proposed model. The model framework could enhance
the performance and interpretability of existing freight mode choice models.
