Toward Reducing Unproductive Container Moves: Predicting Service Requirements and Dwell Times
This article presents the results of a data science study conducted at a container terminal, aimed at reducing unproductive container moves through the prediction of service requirements and container dwell times. We develop and evaluate machine learning models that leverage historical operational data to anticipate which containers will require pre-clearance handling services prior to cargo release and to estimate how long they are expected to remain in the terminal. As part of the data preparation process, we implement a classification system for cargo descriptions and perform deduplication of consignee records to improve data consistency and feature quality. These predictive capabiliti...
| Authors | Elena Villalobos, Adolfo De Unánue T., Fernanda Sobrino et al. |
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| Primary category | cs.AI |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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