通过数据标准化实现生成式人工智能与机器学习协同预测集装箱滞留时间

Generative AI and Machine Learning Collaboration for Container Dwell Time Prediction via Data Standardization

February 24, 2026

作者: Minseop Kim, Takhyeong Kim, Taekhyun Park, Hanbyeol Park, Hyerim Bae

cs.AI

摘要

进口集装箱滞留时间（ICDT）预测是提升集装箱码头作业效率的关键任务，精准预测能有效减少场桥对集装箱的二次搬移操作。实现这一目标需要准确预测单个集装箱的滞留时长，但决定滞留时间的主要因素——货主信息与货物信息——均以非结构化文本形式记录，限制了其在机器学习模型中的有效应用。本研究提出一种生成式人工智能（Gen AI）与机器学习协同的框架以突破此局限。该框架利用Gen AI将非结构化信息标准化为国际通用代码，并通过电子数据交换状态更新触发动态重预测，使机器学习模型能够精准预测ICDT。基于真实集装箱码头数据的大规模实验表明：相较于未使用标准化信息的传统模型，本方法在平均绝对误差指标上提升13.88%。进一步将优化后的预测结果应用于堆场堆存策略，可实现高达14.68%的翻箱率降低，实证了Gen AI提升码头运营效率的潜力。本研究从技术路径与方法论层面为生成式人工智能在港口物流领域的应用提供了有效性与实施思路。

English

Import container dwell time (ICDT) prediction is a key task for improving productivity in container terminals, as accurate predictions enable the reduction of container re-handling operations by yard cranes. Achieving this objective requires accurately predicting the dwell time of individual containers. However, the primary determinants of dwell time-owner information and cargo information-are recorded as unstructured text, which limits their effective use in machine learning models. This study addresses this limitation by proposing a collaborative framework that integrates generative artificial intelligence (Gen AI) with machine learning. The proposed framework employs Gen AI to standardize unstructured information into standard international codes, with dynamic re-prediction triggered by electronic data interchange state updates, enabling the machine learning model to predict ICDT accurately. Extensive experiments conducted on real container terminal data demonstrate that the proposed methodology achieves a 13.88% improvement in mean absolute error compared to conventional models that do not utilize standardized information. Furthermore, applying the improved predictions to container stacking strategies achieves up to 14.68% reduction in the number of relocations, thereby empirically validating the potential of Gen AI to enhance productivity in container terminal operations. Overall, this study provides both technical and methodological insights into the adoption of Gen AI in port logistics and its effectiveness.