Predicting the maneuverability of ships equipped with twin azimuth thrusters remains challenging due to their complex hydrodynamic interactions. This study develops an integrated framework that combines Computational Fluid Dynamics (CFD) with an enhanced Manoeuvring Mathematical Group (MMG) Model. Using the platform supply vessel Hai Yang Shi You 661 as a case study, all requisite hydrodynamic derivatives and propeller coefficients were efficiently obtained through CFD-based captive model tests, including oblique towing and Planar Motion Mechanism tests, conducted in STAR-CCM+ 2206. A core contribution of this work is the systematic evaluation of how hydrodynamic model fidelity affects prediction accuracy. Numerical turning circle simulations were executed with three models of increasing complexity: one with only linear derivatives, a second incorporating nonlinear higher-order terms, and a third, full model that additionally includes nonlinear velocity coupling terms. The results, rigorously validated against full-scale trial data, demonstrate that while the basic CFD-MMG approach is feasible, the inclusion of nonlinear coupling terms is critical for achieving accurate predictions in large-amplitude maneuvers. This enhancement reduced the maximum error in tactical diameter prediction from over 25% to approximately 11.8%. Consequently, this study provides a validated and cost-effective framework for maneuvering the prediction of azimuth-thruster vessels and offers clear, quantitative guidance on the necessary level of model complexity for practical engineering applications.

Accurate behavioral segmentation of vessel trajectories from Automatic Identification System (AIS) is essential for maritime safety and traffic management. Existing methods often rely on predefined thresholds or emphasize geometric criteria and offer limited behavioral interpretability for mobility analysis. This paper introduces an unsupervised behavioral segmentation framework that integrates clustering with matheuristic optimization. Trajectories are cleaned with a forward sliding window, and three smoothed movement features, namely speed, acceleration, and turning rate, are computed for each point. Each feature is discretized by the Jenks Natural Breaks algorithm to extract key feature points and pointwise feature labels. Segment boundaries are near-optimally chosen from these key feature points using a Matheuristic Fixed Set Search (MFSS) that minimizes a Minimum Description Length (MDL) objective. This ensures behavioral consistency within each segment and clear separation between adjacent segments. Experiments on an AIS dataset from the Qiongzhou Strait, China, demonstrate that our proposed method yields more compact, distinctly differentiated segments than baseline methods, while preserving intra-segment behavioral continuity. These segments exhibit strong semantic coherence, making them well-suited for downstream tasks such as traffic risk assessment and route planning.

Short-sea Roll-on/Roll-off passenger (Ro-Pax) corridors rely on tightly interconnected port pairs. These corridors face significant challenges in recovering efficiently after major disruptions, as recovery operations are often managed separately by each port, prioritizing the clearance of local backlogs. This can lead to system-wide inefficiencies due to the operational dependencies between ports and strict navigational rules. To address this challenge, this study develops a cross-port coordinated scheduling model for post-disruption recovery. Taking the Qiongzhou Strait (Xuwen Port–Xinhai Port corridor) as a representative case study, we formulate a mathematical model that jointly optimizes vessel dispatch timing at the departure port and berth assignments at the arrival port, strictly complying with one-way channel and basin safety constraints. An Adaptive Ant Colony Optimization (AACO) algorithm is designed to solve this complex problem. Validation using real post-typhoon data demonstrates that the coordinated strategy outperforms the conventional First-Come-First-Served (FCFS) method, reducing total vessel waiting time by 56.6% and the overall recovery time by 1.8%. This study provides a practical decision-support tool, highlighting how cross-port coordination can significantly improve the operational resilience of short-sea Ro-Pax transport systems during emergencies.