Toward Reducing Unproductive Container Moves: Predicting Service Requirements and Dwell Times

Computer Science > Artificial Intelligence [Submitted on 6 Apr 2026] Toward Reducing Unproductive Container Moves: Predicting Service Requirements and Dwell Times Elena Villalobos (1), Adolfo De Unánue T. (1), Fernanda Sobrino (1), David Aké (1), Stephany Cisneros (1), Jorge Lecona (2), Alejandra Matadamaz (2) ((1) Tecnológico de Monterrey, Mexico City, Mexico, (2) Container Terminal Operations, Veracruz, Mexico) 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 capabilities provide valuable inputs for strategic planning and resource allocation in yard operations. Across multiple temporal validation periods, the proposed models consistently outperform existing rule-based heuristics and random baselines in precision and recall. These results demonstrate the practical value of predictive analytics for improving operational efficiency and supporting data-driven decision-making in container terminal logistics. Comments: Preprint, 20 pages, 9 figures, 5 tables (including appendices) Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Applications (stat.AP) Cite as: arXiv:2604.06251 [cs.AI]   (or arXiv:2604.06251v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.06251 Focus to learn more Submission history From: Elena Villalobos Nolasco [view email] [v1] Mon, 6 Apr 2026 19:06:15 UTC (1,395 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.LG stat stat.AP References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)