To improve the comprehensive evaluation system of ports and move beyond the single evaluation mode of "throughput only," this study investigates a comprehensive evaluation index system for world-class ports. Firstly, the concept and connotation of world-class ports are interpreted using the "Theme-Objective-Path-Service" analysis framework. A comprehensive evaluation index system for world-class ports is proposed, centered around weighted throughput, ship service efficiency, connectivity, economic contribution, and green and security levels. The index weights are determined using the Analytic Hierarchy Process, and a fuzzy comprehensive evaluation model for world-class ports is established. Based on this model, 34 global sample ports are selected for comprehensive evaluation. The evaluation results show that the ports of Singapore and Shanghai rank among the top two in terms of comprehensive scores, placing them in the world's leading lineup. Nine other ports, including Rotterdam, Ningbo-Zhoushan, Busan, and Qingdao, rank among the top ports globally with scores above 80. Finally, suggestions for conducting comprehensive evaluations of world-class ports are proposed, focusing on establishing world-class port evaluation standards and strengthening the application of big data from the Automatic Identification System (AIS).

In order to study the safe deployment of tugboats under the uncontrolled situation of large LNG (Liquefied Natural Gas) ships in the dock-constrained waters, and to ensure the safety of port terminal operations, the study adopts the CFD (Computational Fluid Dynamics) analysis method, based on the wind flow interference model, and calculates the hydrodynamic parameters and determines the emergency tugboat deployment strategy by simulating the drift process of the large LNG ship under different working conditions. Taking Wenzhou Xiaomendao as a case study object, the study focuses on analyzing the ship motion process of Qmax LNG ship under the working condition of sudden loss of control, and calculates the drift distance, which is used to guide the emergency deployment of tugboats. The results of the study show that the emergency deployment strategy of tugboat under different working conditions can be clarified by CFD analysis. This study provides scientific support for the safety of large LNG vessels in port transportation and provides an effective basis for emergency decision-making for port management to ensure the safe operation of ports and shipping industries.

Complex meteorological sea conditions directly affect the safety of ship navigation, and the accuracy of the prediction of offshore wind speed, as a major factor in meteorological sea conditions, is of great significance to the navigation safety and trajectory planning. In order to effectively improve the accuracy of offshore wind speed prediction and overcome the limitations of a single prediction model, the offshore wind form data of Lianyungang station is used as an example study, and the Adaboost algorithm is used to integrate the advantages of multi-models to construct a combined prediction model of offshore wind speed. Four time series prediction models, including BP neural network, GA BPNN, long and short-term memory network and WOA-SVR, are used for wind speed prediction. Considering the prediction effect of a single model, Adaboost algorithm is applied to integrate the GA-BPNN model and WOA-SVR model to construct the combined offshore wind speed prediction model, and the integration accuracy is compared with that of Bagging algorithm. The results show that the root mean square error of the combined prediction model with the Adaboost algorithm is reduced by about 13% and the mean absolute error is reduced by about 16% compared with the single model, which effectively verifies the superiority of the combined prediction model in the prediction of offshore wind speed data, and it is of great significance for the enhancement of navigational safety and the optimization of the trajectory design.

In order to improve the safety of water transportation and reduce collision accidents, a quantitative model of ship collision hazard based on complex network is proposed. By constructing the complex network and calculating the relationship strength to reflect the relationship between ships, the RDF (Radial Distribution Function) is used to analyze the density factor of the ship and discuss the traffic situation around the ship; the centrality of the network is used to analyze the conflict factor of the ship; and the affiliation function based on the power law of Stevens is used to integrate the density factor and the conflict factor to improve the collision hazard quantification method based on ship pairs. In order to prove the accuracy of the model, example verification is carried out and compared with the method based on the superposition calculation of ship pairs, and the results show that the present model has a certain degree of accuracy. By analyzing the actual water situation, the corresponding warning mechanism is discussed, which can be used for monitoring the water traffic situation and hazard warning.

To ensure the safety of navigation in the bridge area, this paper proposes a ship automatic monitoring method based on the fusion of vision and AIS (Automatic Identification System). The ship contour information in the image is extracted by the YOLOv5 (You Only Look Once version 5) target detection algorithm and the Canny algorithm. A distance, azimuth, and height measurement model of the visual target in the bridge area is constructed to achieve the three-dimensional positioning of the ship. An abnormal behavior detection model is established using the ship navigation situation data from the fusion of vision and AIS to automatically identify and monitor monitoring of dangerous ships in the bridge area. The experimental results show that: In cases of single and multiple ships, the accuracy of visual and AIS data association is 98.45% and 91.29%, respectively; The method can effectively monitor the motion state of ships in the bridge area. This paper provides an effective method for ensuring the safety of ships and bridges.

To address the issues of the analytical-based F_QSD (Fuzzy Quaternion Ship Domain) model, such as the difficulty in defining its boundary, insufficient consideration of influencing factors, and challenges in practical application, this paper incorporates the human factor of ship navigator and establishes a D_QSD (Dynamic Quaternion Ship Domain) based on the ship navigator's state to enhance the analytical-based fuzzy quaternion ship domain. The shape parameter K value of the fuzzy quaternion ship domain is calculated according to the ship navigator's state, and the dynamic ship domain model under different ship navigator states is obtained through computer simulation. The results show that the shape and area of the ship domain dynamically change under different driving states. When the ship navigator's state is excellent, the ship domain shape is an irregular rhombus, and the domain area is the smallest. When the ship navigator's state is poor, the ship domain shape is approximately rectangular, and the domain area is the largest. Compared with the traditional analysis-based ship domain, the proposed model determines the fuzzy boundary of the ship domain according to the ship navigator's state, making the dynamic ship domain more flexible and adaptable.

Existing studies indicate that the longer the queue length of vessels, the greater the channel saturation. To predict channel congestion, this study proposes a congestion prediction method that considers the maximum queue length based on the fundamental principles of traffic wave theory. The model utilizes Automatic Identification System (AIS) data to extract traffic flow characteristic parameters and, considering the differences in navigation behavior among ships in different waters, proposes a method for dividing the channel into characteristic areas. The queue length in traffic wave theory is selected as the evaluation index for congestion, and a method for predicting the maximum queue length based on Gaussian process regression is proposed to achieve the prediction of waterway congestion levels. A case study is conducted in the Yuxi River section of the Yangtze River Basin. The results show that the theoretical value of the maximum queue length in this section in July 2020 is 0.98 km, and the Adjusted R² index of the established regression model is 0.88, predicting a maximum queue length of 1.34 km with an error of 0.37 km compared to the theoretical value. The research results demonstrate that the proposed model has a high degree of interpretability and can effectively predict the maximum queue length, thereby enabling the prediction of channel congestion. This study provides a theoretical basis for improving the level of maritime supervision services.

In order to solve the key problem of collision avoidance action of close ships, the steering avoidance process during collision encounter is studied, combining with the ship's maneuvering performance, the time characteristics of the ship's initial turning semicircle elements and turning angle are analyzed; through the decomposition of the relative motion diagram and the inverse approximation algorithm, the mathematical model of the close-quarter situation distance and collision distance is obtained when the ship is steering avoidance. The application of the model is also given; the results of the model application are verified by arithmetic examples and simulation tests. The results show that the proposed two distance models can be used to guide the collision avoidance actions of ships at close range, and provide a theoretical basis for the study of collision risk and the establishment of an automatic collision avoidance decision-making system for ships. At the same time, according to the model, the collision avoidance actions that should be taken by the ship at different stages of the encounter are analyzed for the target ships with different speed ratios and different bearings when there is a collision danger, and the close-range ship collision avoidance action mode is also given. It also gives the model of close-range ship collision avoidance action. It provides the ship deck officers with the support of close-range ship collision danger prediction and ship collision avoidance decision-making. The research results are of great significance to ship navigation safety and navigation intelligence.

Image feature registration is a critical step for stitching and generating large-field images during the inland navigation of ships. To address the problems of sparse water surface feature points and low efficiency in traditional feature matching algorithms for image registration in inland navigation environments, this paper proposes a feature matching method based on image super-resolution reconstruction. Firstly, the input images are subjected to super-resolution reconstruction using generative adversarial networks to enrich image details and increase the number of image feature points. Secondly, the ORB operator and BEBLID algorithm are employed for feature point detection and description. Then, coarse matching is performed based on Hamming distance. Finally, an improved random sampling consistency algorithm is utilized to further eliminate gross errors and purify inliers, achieving robust matching results. The study conducts experiments using five sets of inland navigation environment images with challenges such as low visibility, varying lighting conditions, scale changes, blur, and rotation. The results demonstrate that the proposed approach, leveraging image super-resolution reconstruction for feature point extraction, achieves an increased number of feature points and outperforms comparative algorithms in terms of matching accuracy and speed. This method meets the requirements of high-precision and real-time image matching in inland navigation environments.

With the continuous increase in seaborne trade volume, ship traffic density in port areas is rising, and navigation conditions in port waters are becoming more complex. Short-term ship traffic prediction in port waters is playing an increasingly critical role in ship traffic control and navigation safety management. To address the limitation of low accuracy in aggregate models, this paper, based on ship Automatic Identification System (AIS) data, employs a disaggregate method to construct a hybrid prediction model. This model combines the Long Short-Term Memory (LSTM) network with ships' historical trajectories to calculate short-term ship trajectories in port waters. The counts of ships' trajectories intersecting with an approach channel section are used to predict the short-term ship flow across the section. A numerical example from Ningbo-Zhoushan Port during June to December 2020 demonstrates that the forecasting accuracy of the proposed model reaches up to 80%, significantly higher than that of traditional aggregate models. The model developed here provides a technical foundation for ports to implement ship traffic control methods and improve channel utilization rates.

In a road transport port collection-distribution system involving container trucks and a drayage fleet, it is necessary to conduct joint scheduling of trucks, tractors, and semi-trailers to achieve coordinated optimization of transport structure and routing simultaneously. This paper proposes a joint scheduling optimization model for multi-fleet combined transport, formulated as a mixed integer programming model. A simulated annealing algorithm based on heuristic rules is designed to solve the proposed problem. The impacts of different transport demands on the total transport cost and the number of workable vehicles under different freight modes are explored. Results show that, compared with the single freight mode, the multi-fleet combined freight mode demonstrates significant advantages in effectively reducing transport costs by 13.3% for tasks uniformly distributed in the freight network. Additionally, by optimizing the allocation of its own vehicle resources, the multi-fleet combined transport mode can mitigate the influence of fixed cost weight coefficient changes and transport demand compactness on the total transport cost.

A Vector Auto Regression (VAR) model is constructed by combining the China Containerized Freight Index of E/C America Service (CCFI E/C America Service) and the China Containerized Freight Index of W/C America Service (CCFI W/C America Service) with the Clarksons Container ship Port Congestion Index (CPCI) from January 2018 to February 2023, to quantitatively analyze the impact mechanism of port congestion on container freight rates. The model also incorporates a Vector Error Correction (VEC) model to study the long-run equilibrium relationship between the variables. The results show that: 1) Port congestion leads to the occupation of container capacity and port resources, as well as changes in the distribution of capacity and transportation strategies on the China-U.S. export container routes, which in turn causes different degrees of fluctuations in CCFI on the sub-routes; 2) The effect of port congestion on container freight rates persists for nearly three months; 3) Regardless of the U.S. East route or the U.S. West route, port congestion in the U.S. has a more significant impact on promoting the increase of the container freight index compared to port congestion in China. Meanwhile, this paper provides a new perspective for predicting CCFI by investigating the impact mechanism of port congestion on CCFI fluctuations.

To clarify the development stages and characteristics of China's major coastal ports since its accession to the WTO, a quantitative study is conducted based on the monthly container throughput data of seven major coastal ports from 2002 to 2023. Multivariate change point analysis is introduced to objectively reflect the stage characteristics of port development, and the Chow test is used to verify the reliability of the quantitative research results. The study shows that the major coastal ports in China can be divided into six development stages over the 22-year research period. By combining the characteristics of port container throughput growth in different stages and considering key events such as China's accession to the WTO at the end of 2001, the outbreak of the global financial crisis in 2008, and the spread of COVID-19 in 2020, the six stages are identified as: rapid growth period (2002~2005), fluctuating development period (2006~2010), recovery growth period (2011~2013), stable platform period (2014~2017), development differentiation period (2018~2020), and resilient growth period (2021~2023). Each development stage exhibits distinct characteristics, closely related to the economic and trade conditions of China and the world during the respective periods.

Based on the typical container terminal loading and unloading process, starting from the transport ships, loading and unloading equipments, yard layout parameters, loading and unloading work characteristics, standardized experimental test methods and experimental parameters of energy consumption of loading and unloading machinery in container terminal are formulated, and based on the formulated experimental methods, energy consumption test experiments of loading and unloading machinery in container terminal are carried out, and the sample data of energy consumption of loading and unloading machinery in container terminal are obtained. The kurtosis and skewness test method was applied to analyze and verify the distribution law of the energy consumption test data, and the energy consumption limit value of container terminal loading and unloading equipments was finally obtained based on the statistical quota of the energy consumption test data and the corresponding national standards, combined with the relevant theory of probability theory.

The use of diesel auxiliaries by ships during port calls causes a large amount of fossil energy consumption and pollutant emissions, and the use of shore power for energy supply is a good alternative. For the energy problem of shore power system, the article introduces a hybrid energy system composed of offshore wind turbines, shore power and hydrogen-based energy storage, and proposes a hydrogen-based energy storage planning model based on hybrid stochastic regularization-information gap decision theory. Aiming at the uncertainty of offshore wind turbine output, stochastic planning is used to get the time-sequence typical output scenario; for the difficulty of accurately portraying the probability distribution of shore power port call ship load and shore power price, IGDT is used to form a dual-objective model to deal with the uncertainty of the two as well as to introduce two different risky strategy planning models and analyze them by considering the seasonal factors. The results of the example show that the hybrid energy system can improve energy utilization and interaction, provide a planning basis for decision makers, and verify the effectiveness of the proposed model.

With the development of autonomous navigation for unmanned ships, identifying ship collision avoidance behavior has become a key factor in their independent decision-making. To address the inefficiency and misjudgment issues of existing ship trajectory recognition algorithms, this paper proposes a data mining model based on the steering point of a sliding window for ship collision avoidance. When the model identifies a ship's steering point, it first evaluates the change characteristics of the heading at adjacent time points in the ship's Automatic Identification System (AIS) data using a fixed sliding window. Then, the slope change of the trajectory points at adjacent moments is calculated for verification, and the earliest turning point of the heading change within the window is marked. Finally, a variable sliding window is used to maintain the heading change and error parameters during the trajectory change process, determining whether the steering point is a collision-avoidance steering point. The model is experimentally compared with the Douglas-Peucker (DP) algorithm. The results show that the model can effectively identify whether a ship's steering is collision avoidance behavior, resolve the issue of the DP algorithm misjudging steering points due to data fluctuations, and extract the earliest steering point during the ship collision avoidance process to assist in collision avoidance decision-making. This model can be applied to the research and development of intelligent collision avoidance decision-making systems, ensuring the safety of ship navigation.

The traditional A* algorithm applied to the path planning of offshore wind farm operation and maintenance ships has not yet taken into account the dynamic obstacles, water currents, and crossing navigation channels, therefore, this paper proposes a path planning method that considers navigational risks, heading angle constraints, and path smoothing. On the basis of constructing the map of offshore wind farm water environment by raster method, weight coefficients are introduced to change the proportion of estimated surrogate value in the total cost function of the A* algorithm to achieve the purpose of balancing the strength of heuristic information and shortening the pathfinding time, and the risk of obstacles containing water currents is taken into account in order to improve the actual cost function of the A* algorithm and enhance the security of the planned paths. Meanwhile, the heading angle constraint is considered in the A* algorithm to reduce the total number of traversal nodes, the eight-neighborhood search is constrained to three neighboring nodes conforming to the path direction, the inflection points are extracted and visibility check is performed to remove the redundant inflection points in the path, and the smooth planning path is obtained using a uniform B-spline curve. Taking the Donghai Bridge No.5 and No.6 wind farm waters as an example, a high tide path planning scenario is established, and the operation and maintenance ship needs to pass through 9 wind turbines in order to complete the operation and maintenance tasks; 4 indexes (path length, total risk value of the path, total number of traversed nodes, and total number of inflection points) are utilized for evaluating the planning path, so as to validate the effectiveness of the improved A* algorithm. The simulation results show that in the high tide scenario, the planning path smoothness of the improved A* algorithm is improved by 77.69%, the total risk value of the planning path is reduced by 52.83%, and the total number of traversal nodes is reduced by 30.58%, but the planning path length of the improved A* algorithm is 252.89 m longer than that of the traditional A* algorithm.

With the rapid development of computer technology, numerous new optimization methods and processes have emerged in the field of ship type optimization. However, there is still a lack of open-source and free optimization platforms in China that can efficiently integrate these optimization methods and processes. This article constructs an optimization platform based on the Grasshopper visual programming environment, integrating the fundamental steps of ship type optimization. By incorporating variable complexity methods into the optimization process, the platform addresses issues such as long optimization times and high computational costs, thereby enhancing its functionality and optimization capabilities. Using this platform, drag reduction optimization is performed on the KCS bulbous bow of container ships, and the newly designed ship form demonstrates superior drag performance compared to the original form. This verifies the correctness and feasibility of the platform and lays the foundation for further expansion of its functionalities.

To predict the delivered power of full-formed ships using a combined EFD/CFD method, this work investigated methods of obtaining the hull form factor (1+k) for three full-formed ships under various loading conditions, and analyzed their applicability. The hull form factors of each vessel were obtained using model test and numerical simulation, respectively. They were compared and then utilized to perform full-scale predictions of the delivered power using the combined method. The research indicated that the hull form factor obtained from numerical simulation resulted in a closer match between the predicted full-scale delivered power and the trial data, with an allowable difference. The research also validated the applicability of the delivered power prediction of full-formed ships using the combined method.

To provide integrated communication-navigation means for maritime distress and safety communication, China has established the BDMSS (BeiDou Message Service System) based on the BeiDou system's regional short message capability. After five years of operation, BDMSS has been successfully recognized by the IMO (International Maritime Organization) as a GMDSS (Global Maritime Distress and Safety System) service provider. This paper outlines the evaluation and recognition procedures for mobile satellite systems used in GMDSS, as defined by the IMO and the IMSO (International Mobile Satellite Organization). It examines BDMSS's application process for IMO recognition and the technical and operational assessment conducted by IMSO. Key assessment points for BDMSS, including availability, restoration and spare satellite arrangements, priority mode, on-site demonstration scenario design, and additional considerations, are analyzed. The results enhance understanding of the international approval and evaluation criteria for potential GMDSS mobile satellite systems. Furthermore, they provide valuable references for future revisions of IMO Resolution A.1001(25) and for incorporating international maritime requirements into the design of new mobile satellite systems.

A stochastic programming model is devised for the multi-base, multi-drone location and routing problem, considering the simultaneous movements of drones and ships as well as ship movement uncertainty. A decoding algorithm is developed to divide a sequence into sub-routes using ship-based and drone-based strategies. Furthermore, a bi-stage heuristic algorithm is proposed, combining a genetic algorithm and Tabu search. In the bi-stage algorithm, the first stage addresses ship movement uncertainty and employs Tabu search to solve the drone base station location problem. The second stage uses the genetic algorithm to route the drones for detection based on the location results. Numerical experiment results show that, in the same application scenario, the drone-based (D) strategy can optimize flying distance by 7% while reducing computing time by 50% compared to the ship-based (S) strategy. Considering ship movement uncertainty can reduce flying distance by 10% for the drone base station location solution. Flying distance is sensitive to the number of available drones. For example, in a scenario with two base stations and 3-5 drones, adding one drone may increase flying distance by 15%. Speeding up the drones by 5% may reduce flying distance by 5%. This method can effectively generate multi-UAV inspection paths that meet the requirements of moving ships, providing technical support for maritime supervision.

In response to the IMO Preliminary Strategy for Greenhouse Gas Emission Reduction from Ships and the domestic "3060 Double Carbon Goal", carbon reduction routes applicable to the domestic fleet are proposed. Using the fleet carbon reduction analysis model, the carbon reduction amount, carbon intensity and carbon reduction cost of a domestic shipping company's fleet based on the above fuel routes are analyzed by defining the fossil fuel, methanol fuel and ammonia fuel routes. The results of the study show that the fleet based on methanol and ammonia fuel paths can meet the requirements of the "Preliminary Strategy for Ship Temperature IMO Room Gas Emission Reduction" and the domestic "3060 Double Carbon Goal", and that the fleet can be transitioned from the traditional bunker fuel type to the methanol/ammonia ready type as soon as possible in the near future, and then to methanol/ammonia powered type in the medium and long term. In the near future, we can transition from traditional fuel oil ships to methanol/ammonia fuel ready (methanol/ammonia fuel power system preset) ships as soon as possible, and in the middle and long term, we can gradually transition to methanol/ammonia fuel-powered ships; green methanol and green ammonia have their own advantages, and the number of future medium and long term commercial applications mainly depends on the differences between green methanol and green ammonia in the aspects of availability and economy.

Study on the location selection problem of multi-level offshore ship oil spill emergency equipment depots is of great guiding significance for the effective utilization of oil spill emergency resources, the improvement of regional oil spill emergency response capabilities, and the perfection of the oil spill emergency response system. According to the distribution of oil spill risk points and the predicted oil spill volume, combined with the characteristics of the emergency service radius and comprehensive removal and control capabilities of oil spill emergency equipment depots at different levels, a location selection model for multi-level offshore ship oil spill emergency equipment depots is established with the objectives of achieving the best coverage, the highest reliability, and the strongest timeliness for different risk waters. Then, the MOPSO algorithm and NSGA- Ⅱ algorithm are used to solve the model, and the Pareto optimal solution set is obtained. Starting from the perspective of coordinated rescue and based on the emergency service capabilities of equipment depots at different levels, the location selection model realizes the best multiple coverage of different risk waters, aiming to share emergency resources and further enhance the regional emergency response capabilities.

All-electric tugboats (AETs) produce fewer carbon emissions, but their battery charging requirements can lead to ship delays, which may increase the overall carbon emission costs in ports. Therefore, it is essential to study the carbon emission reduction performance of AETs in ports and compare their effectiveness with that of diesel tugboats (DTs). This paper establishes scheduling optimization models for AETs and DTs, respectively, using the sum of tugboat carbon emissions and ship delay carbon emissions as the objective function. Taking a day's data from three port areas in Ningbo-Zhoushan Port as an example, the Gurobi solver is employed to find the optimal solution. A comparative study is conducted on the minimum tugboat carbon emission cost and total carbon emission cost when AETs and DTs with the same horsepower and quantity are used to handle the same ships. The results show that if only the carbon emission cost of tugboats is considered, AETs can reduce carbon emission costs by 9.5% to 22.9% compared to DTs. However, when considering the overall carbon emission cost of the port areas, AETs still demonstrate good carbon emission reduction effects when handling fewer than 8 ships. When the number of ships exceeds 8 and the port is busy, the charging requirements of AETs increase the overall carbon emission cost of the port by 24.68%, which is not conducive to port carbon emission reduction.