Generative AI and Machine Learning Collaboration for Container Dwell Time Prediction via Data Standardization
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.
699e89c1dfbcf0b800aecb9b
Minseop Kim
699e89c1dfbcf0b800aecb9c
Takhyeong Kim
699e89c1dfbcf0b800aecb9d
Taekhyun Park
claimed_verified
2026-02-25T17:29:08.802Z
64ba75761d0a5a5760874197
https://cdn-avatars.huggingface.co/v1/production/uploads/64ba75761d0a5a5760874197/5V3jxX-vim-wMaU2Zjz5h.jpeg
TaekHyunPark
Thrillcrazyer
user
Thrillcrazyer
699e89c1dfbcf0b800aecb9e
Hanbyeol Park
699e89c1dfbcf0b800aecb9f
Hyerim Bae
Generative AI and Machine Learning Collaboration for Container Dwell Time Prediction via Data Standardization
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.
A collaborative framework integrating generative artificial intelligence with machine learning improves container dwell time prediction by standardizing unstructured text data, leading to reduced rehandling operations in container terminals.
generative artificial intelligence machine learning unstructured text electronic data interchange container dwell time re-handling operations container stacking strategies