Maritime navigation efficiency is commonly assessed using isolated route, speed, energy, or traffic indicators that do not fully represent voyage context. This study proposes a context-aware framework based on GPS–AIS data fusion, planned-route geofencing, metocean information, and encounter-based validation. The Navigation Efficiency Resilience Index (NERI) combines target achievement, trajectory-derived response activity, and disturbance intensity into a bounded, time-resolved diagnostic index. The framework was evaluated using a Singapore–Montevideo container-ship voyage with 30 s position data, surrounding-vessel AIS, corridor-specific cross-track limits, and collocated metocean variables. The voyage-level mean NERI was 0.679, and its 10th percentile was 0.519. Lower values occurred mainly in constrained waters, approach areas, and the metocean-intensive Cape transition, whereas the Indian Ocean and South Atlantic legs achieved higher mean values of 0.704 and 0.736, respectively. For the analysed datasets, the regular own-ship position record produced more stable trajectory-derived indicators than the less regularly sampled own-ship AIS series, without implying an inherent accuracy advantage. The full NERI formulation achieved an AUROC of 0.83 and an AUPRC of 0.41 for CPA/TCPA conflict-window classification. NERI therefore provides a decomposable, plan-relative analytical layer for retrospective voyage monitoring and diagnostics, but it is not a direct safety or collision-risk measure.
With the growing interest in Maritime Autonomous Surface Ships (MASS), numerous risk-assessment models have been proposed for route planning and hazard avoidance during navigation. Existing models, however, generally share two limitations. First, many assess risk from information such as vessel traffic and therefore do not adequately reflect the marine weather and sea state itself. Second, they often consider only one or two factors, such as wave height or wind, and even when several factors are merged into a single value, it is difficult to trace back why the result is dangerous. To overcome these limitations, this study proposes a Meteo-Hydrological Fusion Index (MHFI) that combines five environmental factors—wave height, swell period, current, wind, and visibility—into a single risk value. Each factor is first mapped to a 0–4 risk score, and the three highest scores at a given location are then combined by a weighted sum. This summarizes composite risk in a single value while preserving the ranking of the factors that produced it, so the result remains traceable. Applying the index to the coastal waters of Mokpo, we confirmed that the dominant factor behind a given risk grade varies with time and location, and that a rapid, area-wide rise in risk over a short period can be captured by a single indicator. We further show how risk varies along the main fairway and how the index can be overlaid on a display panel. These results indicate that the MHFI could serve as a decision-support layer in an S-100-based digital navigation environment.
Ahra Kim, Yeonju Jeong, Namkyun Im· Journal of Marine Science an...· 0 citations
With the rapid advancement of intelligent shipping, autonomous navigation in complex and confined waters has become a critical challenge. This study aims to develop a robust autonomous navigation decision-making method to address the combined effects of tidal variations, water depth gradients, and restricted maneuvering ability in shallow waters. A digital traffic environment is constructed by fusing real-time automatic identification system (AIS) data with electronic chart display and information system (ECDIS) information and incorporating tidal effects, thereby enabling spatiotemporal situational awareness for autonomous navigation decision-making. The methodology quantitatively interprets collision avoidance rules and navigational best practices to determine optimal maneuvering thresholds for typical encounter scenarios in restricted waters. By coupling ship kinematic characteristics with bathymetric features, a three-dimensional ship domain model is developed, incorporating squat effects and under-keel clearance requirements, whereas a risk quantification algorithm accounts for water depth gradient transitions. The experimental results show that this method performs reliably in complex shallow waters. The proposed
perception-decision-execution-feedback
framework enables rapid information updates and allows the system to adapt to uncoordinated actions of target ships, handle residual errors, maintain a safe distance between ships, and reduce potential collision risk. A virtual-real integrated scenario based on AIS and ECDIS data is established to systematically validate the proposed method. It provides reliable theoretical and methodological support for the theoretical research and engineering application of autonomous navigation technology in complex shallow waters.
Kexin Xu, Yixiong He, Xingya Zhao et al.· ASCE-ASME Journal of Risk an...· 0 citations
Maritime administrations frequently implement short-term intensive safety actions to mitigate collision risk in mixed commercial–fishing traffic waters. However, empirical evidence on their effectiveness remains limited because maritime accidents are rare and behavior-level risk indicators are not routinely incorporated into policy evaluation. This study develops an AIS-based evaluation framework that uses monthly near-miss counts as a behavior-level proxy of navigational risk and combines this proxy with a difference-in-differences (DID) design to assess a maritime special safety action in Ningbo–Zhoushan waters. Using large-scale AIS trajectory data, near-miss events are identified based on DCPA and TCPA criteria and then aggregated to the sea area–month level. The analysis covers 9 sea areas from January to December 2023 (108 observations). In the baseline two-way fixed-effects specification, the coefficient on Treat × Post is positive but statistically insignificant (β = 0.4094, SE = 0.2581), indicating that the intervention did not produce robust evidence of a reduction in the AIS-based near-miss indicator. Event-study estimates likewise show no statistically significant persistent dynamic treatment effect within the observation window. These findings suggest that, under the proxy measure and identification strategy used in this study, the special safety action did not generate a clearly identifiable reduction in near-miss counts in treated waters. Methodologically, the study demonstrates the practical value of combining AIS-derived behavioral indicators with quasi-experimental policy evaluation. At the same time, the results should be interpreted with caution, because near-miss counts are structurally related to traffic intensity and traffic composition, and the policy period overlaps with the seasonal fishing moratorium. The proposed framework nevertheless offers a useful basis for evidence-based evaluation of non-engineering maritime safety interventions in complex mixed-traffic environments.
Tunan Xu, Yifei Mao, M. Grifoll et al.· Frontiers in Marine Science· 0 citations
Abnormal ship behavior detection is important for maritime traffic surveillance, navigation safety, and risk prevention. However, existing methods often depend on handcrafted features or a single reconstruction or prediction signal, which limits their ability to detect both sustained trajectory abnormalities and abrupt vessel movement changes. This paper proposes a Dual-Error Fusion LSTM–Transformer framework, referred to as DEFLT, for AIS-based abnormal ship behavior detection. A motion-aware vessel representation was first constructed by combining the geographical position, speed over ground, course over ground, and their temporal variations. An LSTM autoencoder reconstructs historical trajectory windows, while a Transformer prediction module estimates subsequent vessel states. The standardized reconstruction and prediction errors are fused into a unified anomaly score to capture complementary evidence from historical trajectory inconsistency and unexpected future motion. Experiments were conducted using real-world AIS data collected during September 2019 from four representative Danish waters. The study considers four abnormal behaviors: speed anomalies, course anomalies, loitering, and route deviations. Compared with KNN, LOF, Isolation Forest, Random Forest, the LSTM-AE, and the Transformer, DEFLT achieves F1-scores of 0.96, 0.97, 0.88, and 0.93 across the four study areas. For type-specific detection, the Macro-F1 values range from 0.61 to 0.86, while Macro-Recall remains between 0.88 and 0.96. Friedman and post hoc Wilcoxon signed-rank tests further demonstrate that DEFLT provides a significant and consistent improvement over all baseline methods. These results verify the effectiveness of dual-error fusion for detecting heterogeneous abnormal ship behaviors from AIS trajectories. In operational settings, DEFLT can serve as an alert-prioritization tool for vessel traffic services and port authorities by directing attention to atypical trajectories that require timely review, thereby supporting safer and more resource-efficient maritime traffic coordination.
Large-scale maritime activity, persistent shipping incidents, and complex marine environments continue to place substantial demands on maritime search and rescue (MSAR). Current MSAR systems do not fully capitalize on the complementary strengths of unmanned aerial vehicles (UAVs) and satellites for collaborative tracking and rescue support. Existing air-space collaboration technologies suffer from two critical limitations: (1) rigid processes, including fixed task allocation, pre-determined path planning without real-time environmental adaptation, and isolated satellite–UAV decision-making, and (2) long task completion cycles, mainly because many methods are adapted to wide-area, long-duration military tracking scenarios. They therefore provide limited support for the dynamic flexibility required in MSAR. This study proposes a Proximal Policy Optimization (PPO)-based air-space collaborative tracking method for maritime moving targets to address these shortcomings and enhance air-space cooperation in MSAR operations. The core implementation of the method includes: (1) integration of target drift forecasting, satellite orbit prediction, UAV task allocation, and path planning into a unified reinforcement learning framework to reduce isolated single-platform decision-making; (2) the adoption of PPO to generate dynamic and flexible air-space collaborative tracking strategies that adjust satellite observation angles and scanning ranges, as well as UAV altitude, speed, and heading according to real-time target, environmental, and platform states; and (3) the design of a multi-dimensional reward function that balances target proximity, energy efficiency, coverage overlap, and inter-platform cooperation to guide strategy optimization. Simulation experiments include system-feasibility verification, baseline-controller comparison, PPO hyperparameter screening, and cross-scenario evaluation. Under idealized communication and payload-matching assumptions, the method enables coordinated tracking of maritime moving targets in simulated MSAR scenarios. In the standardized evaluation, PPO achieved an 11.9% higher mean evaluation episode return, 11.2% lower aggregate UAV energy consumption, and a 9.92-percentage-point greater endurance margin than DDPG. Hyperparameter screening compared candidate learning rates, discount factors, and training budgets, informing the PPO configuration for the subsequent six-scenario evaluation. Across the six controlled scenarios, rewards stabilized after approximately 1400 steps, while action magnitudes varied among regions. These results indicate that the proposed method has potential to enhance air-space collaborative tracking for MSAR decision support.
Zhaoyang Liao, Z. Du, Hongyuan Zeng et al.· Journal of Marine Science an...· 0 citations
To address the limited endurance of Unmanned Surface Vehicles (USVs) in time-varying sea conditions, this study investigates global energy-efficient path planning that balances obstacle avoidance and energy efficiency. First, based on ship seakeeping theory, an energy consumption model incorporating wave height, wave period, speed, and wave-encounter angle is constructed to represent the impact of dynamic sea states on resistance. The model is assessed through formula–program consistency verification, a multi-factor input ablation on a physics-constrained benchmark, and external trend validation against published towing-tank added-resistance data. Second, the planning problem is modeled as a Partially Observable Markov Decision Process (POMDP). Built upon the Soft Actor-Critic (SAC) algorithm, a Bimodal Spatio-Temporal Feature Fusion Network (BSTFN) is proposed to achieve deep fusion of spatial perception information and historical temporal sea state sequences for decision-making. Furthermore, a composite reward function is designed, integrating energy consumption penalties, heading guidance, and smoothness constraints. Simulation results demonstrate that the proposed method effectively utilizes favorable encounter angles to avoid high sea state regions. While maintaining high task success rates, it significantly reduces average energy consumption, effectively enhancing the endurance and robustness of USVs in complex dynamic environments.
Zhaohui Liu, Qing Li· Engineer· 0 citations
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