A new study uses historical operational data to predict container dwell times and pre-clearance service needs. Researchers implemented a classification system for cargo descriptions and deduplicated consignee records to refine feature quality. These models help terminal operators reduce unproductive container moves. The result offers a niche application of machine learning for logistics optimization.