Application of Large Language Models for Container Throughput Forecasting: Incorporating Contextual Information in Port Logistics

TL;DR

This paper explores using large language models to forecast container throughput in port logistics by incorporating contextual operational information, demonstrating improved accuracy over traditional models.

Contribution

It introduces a novel prompt structure for LLMs tailored to port logistics and validates its effectiveness through extensive experiments.

Findings

LLMs outperform benchmark models in throughput forecasting

Contextual information enhances LLM inference accuracy

Effective utilization of multiple layers of port operational data

Abstract

Recent advancements in generative artificial intelligence (AI) have demonstrated its substantial potential in various fields. However, its application in port logistics remains underexplored. Ports are complex operational environments where diverse types of contextual information coexist, making them a promising domain for the implementation of generative AI and highlighting the urgency of related research. In this study, we applied a large language model (LLM)-a leading generative AI technique-to forecast container throughput, which is a critical challenge in port logistics. To this end, we adopted a state-of-the-art LLM approach and proposed a novel prompt structure designed to incorporate the contextual characteristics of port operations. Extensive experiments confirm the superiority of our method, showing that the proposed approach outperforms competitive benchmark models. Furthermore, additional experiments revealed that LLMs can effectively learn and utilize multiple layers of contextual information for inference in port logistics. Based on these findings, we explore the key constraints affecting LLM adoption in this domain and outline future research directions aimed at addressing them. Accordingly, we offer both technical and practical insights to support the effective deployment of generative AI in port logistics.