A new study at a container terminal uses machine learning to predict pre-clearance service needs and cargo dwell times. Researchers implemented a classification system for cargo descriptions and deduplicated consignee records to refine feature quality. These models leverage historical operational data to reduce unproductive container moves. The approach helps terminal operators optimize strategic planning and yard space.