用机器学习预测集装箱服务需求和停留时间，减少无效移动。
Toward Reducing Unproductive Container Moves: Predicting Service Requirements and Dwell Times
- 基于历史数据构建预测模型，识别需预清关的集装箱。
- 在多个时间周期内，准确率和召回率均优于规则基线。
- 适合港口运营优化与智能调度系统开发者参考。
本文针对集装箱码头运营中的无效移动问题，开展数据科学研究，提出通过预测服务需求与集装箱停留时间来优化作业流程。我们构建并评估了利用历史运营数据的机器学习模型，可提前预判哪些集装箱需在货物放行前完成预清关处理，并估算其在码头的预计停留时长。数据准备阶段，我们对货物品名进行分类，并对收货人记录去重，以提升数据一致性和特征质量。多项时间验证结果显示，所提模型在精确率与召回率上持续优于现有规则基线与随机基准。该研究展示了预测分析在提升码头运营效率及支持数据驱动决策方面的实际价值。
原文摘要 · Abstract (English)
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.
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