Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques

no code implementations • 1 Feb 2024 • Diyi Liu, Hyeonsup Lim, Majbah Uddin, Yuandong Liu, Lee D. Han, Ho-Ling Hwang, Shih-Miao Chin

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.

Ensemble Learning Survey