JIN, JIONGCHAO (Agency for Science, Technology and Research), FU, xiuju (Institute of High Performance Computing), Gao, Xiaowei (Imperial College London), Cheng, Tao (University College London), Yan, Ran (Nanyang Technological University)

MSD-LLM: Predicting Ship Detention in Port State Control Inspections with Large Language Model

Scheduled for presentation during the Invited Session "S18c-Innovative Applications of LLM in Multimodal Transportation Systems" (TH-LA-T18), Thursday, November 20, 2025, 16:00−16:20, Southport 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Maritime transportation is the backbone of global trade, making ship inspection essential for ensuring maritime safety and environmental protection. Port State Control (PSC),conducted by national ports, enforces compliance with safety regulations, with ship detention being the most severe conse￾quence, impacting both ship schedules and company reputations. Traditional machine learning methods for ship detention pre￾diction are limited by the capacity of representation learning and thus suffer from low accuracy. Meanwhile, autoencoder based deep learning approaches face challenges due to the severe data imbalance in learning historical PSC detention records. To address these limitations, we propose Maritime Ship Detention with Large Language Models (MSD-LLM), integrating a dual robust subspace recovery (DSR) layer-based autoencoder with a progressive learning pipeline to handle imbalanced data and extract meaningful PSC representations. Then, a large language model groups and ranks features to identify likely detention cases, enabling dynamic thresholding for flexible detention predictions. Extensive evaluations on 31,707 PSC inspection records from the Asia-Pacific region show that MSD-LLM outperforms state of￾the-art methods more than 12% on Area Under the Curve (AUC) for Singapore ports. Additionally, it demonstrates robustness to real-world challenges, making it adaptable to diverse maritime risk assessment scenarios.