The continuous increase in crisscross navigation between passenger and cargo ships poses a significant threat to navigation safety in restricted inland waters. Traditional row-by-row crossing operation methods are inadequate for ships navigating such complex environments, leading to a surge in navigational risks. This study proposes an enhanced row-by-row following ship crossing operation method, building upon traditional approaches to address the dual peak periods of passenger ship departures and tidal effects. Based on traffic conflict technology and dynamic ship domain theory, large-angle and small-angle row-by-row following ship crossing models were developed. The advantages of the proposed methods are validated using actual Automatic Identification System (AIS) data collected from the turnaround area of the busy Huangpu River. Results indicate that both the large-angle and small-angle row-by-row crossing methods effectively mitigate the safety limitations of traditional methods. Furthermore, the small-angle row-by-row crossing method improves passenger ship crossing efficiency by up to 50% compared to the large-angle method. The proposed row-by-row following vessel crossing operation method demonstrates significant potential for enhancing navigation efficiency and safety in restricted inland waterways, particularly in congested turnaround areas.

Autonomous berthing is a key element of intelligent navigation, yet its strong scenario dependence limits the application of theoretical research into actual implementations. Variations in ship type, propulsion configuration, and berth conditions impose distinct requirements on trajectory planning and control. At the same time, defining the completion criteria for autonomous berthing operation and establishing a comprehensive evaluation framework are essential for ensuring practicality and safety. This paper systematically reviews recent advances in trajectory planning and motion control for autonomous berthing. First, the key technical elements, including trajectory planning and motion control methods, are introduced. Second, different berthing strategies tailored to specific ship types and propulsion systems are analyzed in depth. Subsequently, berthing completion standards, performance evaluation metrics, and experimental validation approaches are discussed. Finally, the major challenges in the current research are summarized, and potential directions for future development are outlined.

As an important component of the waterway transportation system, Ro-Ro passenger ship transportation plays a significant role in inland river, coastal, and even cross-strait transport services. In recent years, collisions involving Ro-Ro passenger ships have occurred from time to time. To mitigate the losses caused by such accidents, this paper proposes an emergency decision-making model for Ro-Ro passenger ship collisions based on a fuzzy Bayesian network. The identified emergency decision variables for RoPax ship collisions are fuzzified by introducing fuzzy logic. Combined with improved IF-THEN rules, confidence rule bases are established and then converted into a conditional probability table, thereby constructing a complete Bayesian network inference structure. Ultimately, the optimal emergency decision scheme is determined through utility value evaluation. The results demonstrate that the proposed emergency decision-making model is effective and feasible, aligning with practical application requirements. This study provides ship decision-makers with a reference basis for emergency response in the event of a RoPax ship collision.

Currently, ship navigators can assess flow patterns using basic instruments and adjust maneuvering strategies accordingly. Access to detailed flow field data of a waterway can provide valuable information and early warnings for ships transiting the area. This study analyzes surface flow in the waters near Jianghan Bridge, captured by video. By employing Large-Scale Particle Image Velocimetry (LSPIV), a method is developed to measure surface flow velocity in the navigation channel, enabling analysis of surface flow characteristics and acquisition of surface flow field data. The obtained flow field data are validated through comparison with optical flow methods and Acoustic Doppler Velocimetry. Results demonstrate that the proposed surface flow velocity measurement method can effectively capture detailed flow pattern characteristics of surface currents in the study area. This approach provides data support for navigation and path planning of both conventional ships and smart ships utilizing big data, contributing practical value to the enhancement of maritime safety and operational efficiency.

To support the autonomous navigation of cargo-carrying vessels with specific time and position requirements, research on high-precision trajectory tracking control is necessary. In response to the limited existing studies on cargo vessels and the insufficient consideration of actuator characteristics-where thrust and torque are often treated as directly controllable inputs, leading to limited practical feasibility-a control method combining a virtual vessel leader and an Integral Line-of-Sight (ILOS) approach is proposed. This method uses a propeller speed prediction algorithm to synchronize the real vessel with the virtual vessel and employs speed feedback correction to compensate for disturbances. To improve tracking accuracy, the relative positions are utilized to determine the desired heading through the improved ILOS method, thereby reducing the problem to one of course keeping. Ultimately, vessel trajectory tracking is achieved. Simulation results show that the controlled vessel accomplishes trajectory tracking under disturbance, with a steady-state error of less than ±0.5 m, an error convergence time of less than 50 s, an 86% reduction in rudder jitter, and 71% reduction in propeller speed jitter. The proposed control method is straight forward, demonstrating high performance, can serve as a valuable reference for engineering applications.

Maritime fire incidents pose a significant threat to the safety of ships, with human factors being the primary cause of these accidents. Accurately identifying the emotional changes of crew members in maritime fire scenarios is of great significance for enhancing their firefighting capabilities. Virtual reality technology is employed to simulate maritime fire scenes and collect Electroencephalogram (EEG) signals from multiple subjects. The EEG signals are preprocessed and decomposed into sub-signals of different frequency bands using discrete wavelet transform. Three features, including mean absolute value, standard deviation, and root mean square, are extracted from each sub-frequency band to establish a feature set. Multiple machine learning models suitable for emotion recognition are constructed, and the models are evaluated using metrics such as precision, accuracy, and F₁ score. Experimental results show that the support vector machine classification model performs the best, with an accuracy of 87.97%, which significantly improves the three-class classification problem of crew members' fear emotions in maritime environments. Combining virtual reality technology with EEG emotion recognition techniques can effectively induce and identify crew members' fear emotions in fire scenarios. This method is beneficial for assessing and improving the emergency response capabilities of crew members in firefighting training.

To study the pitching stabilization performance of super-large ships under severe sea conditions, this paper takes the tanker "KVLCC2" as the research object. A weighting matrix is utilized to stabilize its transfer function model in Mathematica, and the stability of the model is verified using the root trajectory shaping method. Subsequently, a simplified first-order closed-loop gain-shaping algorithm is applied to design the robust controller. In addition, a dual nonlinear feedback control algorithm is proposed to be incorporated into the control system to further enhance its pitching stabilization performance. To validate the effectiveness of the dual nonlinear feedback control system for pitching stabilization, wind scale of 7 and 8 wind and wave models along with perturbation links are introduced into the system for simulation experiments. The experimental results demonstrate that even with a time lag constant of 0.15, the dual nonlinear feedback control system effectively improves the ship's pitching stabilization performance under rough sea conditions. The proposed dual nonlinear feedback control system can provide technical support for the smooth and efficient navigation of super-large ships in varying sea conditions.

In the context of carbon emission reduction, this study constructs a bi-level planning model for port microgrid investment and deployment, with the government as the upper-level decision-maker and the port area as the lower-level follower. The model incorporates the interests of the port area, including berthed vessels, and aims to maximize environmental benefits while minimizing the total cost of the port area over the planning period. Using the Column and Constraint Generation (CCG) algorithm, the optimal investment and operation strategy for the port area during the planning horizon is derived. The study analyzes the deployment of the port microgrid system under varying incentive budgets and evaluates the resulting environmental benefits, comparing the effectiveness of different incentive strategies. The results demonstrate that a hybrid incentive strategy can significantly enhance investment motivation in port microgrid systems, thereby effectively fostering innovation in the energy structure of the port region and accelerating the emission reduction process.

Accurate forecasting of port container throughput is of great significance for port operators and government administrations in making scientific decisions. Existing forecasting methods, however, often pay insufficient attention to short-calendar-time PCT and exhibit limited accuracy in handling nonlinear and non-stationary fluctuation series. This paper takes the container throughput of Shanghai Port as the research object and proposes a novel deep learning model based on secondary decomposition using CCVMD and STL. Using the correlation coefficient as a reference, variational mode decomposition is first applied to the original time series. Subsequently, a secondary decomposition divides the data into seasonal, trend, and residual components. An algorithm-optimized long short-term memory neural network is then employed to predict each component separately, and the final prediction results are aggregated. Experimental results show that the combined decomposition model with data preprocessing significantly outperforms other models in PCT forecasting. The proposed model achieves a mean absolute percentage error of 0.021 703, a root mean square error percentage of 0.026 852, and a mean absolute error percentage of 0.022 14, indicating superior overall performance compared to 12 benchmark models and several models from prior studies. Furthermore, the secondary decomposition approach demonstrates enhanced reliability in tracking extreme values, removing and reducing noise, and improving interpretability.

From the aspects of navigation service, reservation navigation, green and low carbon, the service level index system of inland navigation hub under the reservation mode of 6 first-level indicators and 22 second-level indicators is constructed. Secondly, the game comprehensive weighting method is used to combine the qualitative weights and quantitative weights determined by the interval two-tuple linguistic method and the CRITIC method respectively. Then, based on the matter-element extension theory, the service level evaluation model of inland navigation hub under the reservation mode is constructed. Finally, taking the Three Gorges navigation hub of the Yangtze River as an example, the empirical analysis is carried out to verify the scientificity and feasibility of the model. The research conclusion has a good reference value for improving the service level of inland navigation hub.