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.