基于数据标准化的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预测是提升码头作业效率的关键任务，精准预测能有效减少场桥翻箱作业。实现这一目标需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，限制了机器学习模型的有效利用。本研究提出生成式人工智能与机器学习协同的解决方案，通过Gen AI将非结构化信息标准化为国际代码，并利用电子数据交换状态更新触发动态重预测，使机器学习模型能精准预测ICDT。基于真实码头数据的实验表明：相较于未使用标准化信息的传统模型，该方法在平均绝对误差指标上提升13.88%；将改进后的预测应用于堆存策略，可实现翻箱次数最高减少14.68%，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洞见。

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.