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

2026-02-24

AI Summary

This study addresses the challenge of predicting import container dwell times, which has been hindered by the unstructured textual nature of shipper and cargo information. To overcome this limitation, the paper introduces generative AI into port logistics for the first time, proposing a synergistic framework that integrates generative AI with machine learning. Specifically, generative AI is employed to standardize unstructured text into international coding schemes, and this structured data is combined with EDI status updates to enable dynamic re-prediction of dwell times. Evaluated on real-world terminal data, the approach reduces the mean absolute error in dwell time prediction by 13.88%. When incorporated into stacking strategies, it further decreases rehandling operations by 14.68%, significantly enhancing terminal operational efficiency.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• container dwell time prediction
• unstructured text
• data standardization
• import container
• terminal productivity

Innovation

• Generative AI
• Data Standardization
• Container Dwell Time Prediction
• Machine Learning Collaboration
• Port Logistics Optimization