Typhoon avoidance is critical for ship maneuvering safety under extreme meteo-ocean conditions. This study proposes a data-driven framework that converts AIS trajectories into interpretable course deviation and speed change responses for navigational decision support. After AIS cleaning, temporal resampling, and matching with gridded wind, wave, and current fields, rule-based sliding-window and regression procedures, informed by experienced captains and company staff, automatically generate proxy labels for deviation and speed reduction. Samples are stratified by vessel size to reflect differences in inertia and maneuverability, and XGBoost classifiers are trained with simple resampling to mitigate class imbalance. The framework is demonstrated on a single-event case study of Typhoon Yagi in the South China Sea, covering 8609 vessels and reconstructed sailing fragments. On the test set, the deviation model achieves 89.8% accuracy and high recall for deviation cases, while the speed change model reaches 82% balanced accuracy under the proxy-label setting. Results suggest a scale-dependent response: smaller vessels exhibit more frequent course deviation, whereas larger vessels more often reduce speed under severe wind-wave loading. The framework offers a proof-of-concept approach to derive behavior-based indicators from AIS and environmental data and may support situational assessment under adverse weather.

The inherent complexities of Artificial Intelligence (AI) and machine learning (ML) technologies expose autonomous ships to a wide range of multifaceted interconnected risks. However, very few studies have aimed at the holistic risk assessment of autonomous ships. To this end, this study employs an expert-opinion-based integrated machine learning approach amalgamating logistic regression and Bayesian network to conduct risk assessment for autonomous ships. The results reveal human factor interactions and operational issues as the prominent accident causation factors. The findings of this study will contribute significantly to the existing literature on autonomous ships and the complexities involved in their operational systems. By identifying critical factors causing accidents and their impact on autonomous ship safety and resilience, stakeholders such as autonomous ship manufacturers, port authorities, shipping companies, and governments can develop more efficient and effective operational and safety systems.

Tropical cyclones reduce port capacity and leave heterogeneous congestion. Yet empirical measurements of port resilience are rarely connected to operational strategy evaluation on the same observed event baseline. This study develops a dual-layer framework that measures event-level operational resilience under prevailing practice and estimates modelled marginal improvements from three operational levers. Aggregate baseline measures calibrate multi-server queues for 28 ports without event-period tuning, and discrete-event simulation replays 178 port-event trajectories. The calibrated representation places heterogeneous ports on a common queueing scale. Measured states are benchmarked against a dynamic four-hour business-as-usual (BAU) baseline that retains normal temporal variation. During exposure, median service-capacity loss reaches 0.627, and sustained operational-capacity restoration is confirmed after a median 44 h. Service-focused recovery (SES), coordinated recovery (CRS), and two-stage proactive–reactive response (TPRS) are compared after matching costs within each event. CRS gives the largest mean reduction in cumulative queue burden under the base-case cost coefficients and ranks first in 113 of 128 cost-sensitivity scenarios. SES leads when capacity coordination becomes sufficiently expensive, while TPRS becomes more competitive under prolonged, high-loss exposure. The framework supports strategy assessment according to event state, operational feasibility, and implementation cost.