Abstract: As tropical cyclones intensify and track forecasts become increasingly uncertain, U.S. ports face heightened supply-chain risk under extreme weather conditions. Port operators need to rapidly synthesize diverse multimodal forecast products, such as probabilistic wind maps, track cones, and official advisories, into clear, actionable guidance as cyclones approach. Multimodal large language models (MLLMs) offer a powerful means to integrate these heterogeneous data sources alongside broader contextual knowledge, yet their accuracy and reliability in the specific context of port cyclone preparedness have not been rigorously evaluated. To fill this gap, we introduce CyPortQA, the first multimodal benchmark tailored to port operations under cyclone threat. CyPortQA assembles 2,917 realworld disruption scenarios from 2015 through 2023, spanning 145 U.S. principal ports and 90 named storms. Each scenario fuses multisource data (i.e., tropical cyclone products, port operational impact records, and port condition bulletins) and is expanded through an automated pipeline into 117,178 structured question answer pairs. Using this benchmark, we conduct extensive experiments on diverse MLLMs, including both open-source and proprietary model. MLLMs demonstrate great potential in situation understanding but still face considerable challenges in reasoning tasks, including potential impact estimation and decision reasoning.

Chenchen Kuai is currently a second-year Ph.D. student in Transportation Engineering at Texas A&M University, in the Zachry Department of Civil and Environmental Engineering. He works under the supervision of Dr. Yunlong Zhang, focusing on data-driven and AI-enabled approaches for freight systems and freight resilience. Prior to beginning his doctoral studies, he completed his undergraduate and Master's in Transportation Engineering from Southeast University and University of California, Los Angeles. He develops datasets and tailored AI models that combine vision, spatiotemporal, and network topology information to support large-scale transportation system monitoring and decision-making.