Search Results (170)

Accurate ship berthing behavior prediction (BBP) is essential for enabling collision warnings and support decision-making. Existing methods based on Automatic Identification System (AIS) data perform well in the task of ship trajectory prediction over long time-series and large scales, but struggle with addressing the fine-grained and highly dynamic changes in berthing scenarios. Therefore, the accuracy of BBP remains a crucial challenge. In this paper, a novel BBP method based on Light Detection and Ranging (LiDAR) data is proposed. To test its feasibility, a comprehensive dataset is established by conducting on-site collection of berthing data at Dalian Port (China) using a shore-based LiDAR system. This dataset comprises equal-interval data from 77 berthing activities involving three large ships. In order to find a straightforward architecture to provide good performance on our dataset, a cascading network model combining convolutional neural network (CNN), a bi-directional gated recurrent unit (BiGRU) and bi-directional long short-term memory (BiLSTM) are developed to serve as the baseline. Experimental results demonstrate that the baseline outperformed other commonly used prediction models and their combinations in terms of prediction accuracy. In summary, our research findings help overcome the limitations of AIS data in berthing scenarios and provide a foundation for predicting complete berthing status, therefore offering practical insights for safer, more efficient, and automated management in smart port systems. Full article

With the continuous expansion of global trade, achieving sustainable maritime transport optimization and operations has become a key strategic direction for transforming maritime transport companies. To summarize the current state of research and identify emerging trends in sustainable maritime transport optimization and operations, this study systematically examines representative studies from the past decade, focusing on three dimensions, technology, management, and policy, using data sourced from the Web of Science (WOS) database. Building on this analysis, potential avenues for future research are suggested. Research indicates that the technological field centers on the integrated application of alternative fuels, improvements in energy efficiency, and low-carbon technologies in the shipping and port sectors. At the management level, green investment decisions, speed optimization, and berth scheduling are emphasized as core strategies for enhancing corporate sustainable performance. From a policy perspective, attention is placed on the synergistic effects between market-based measures (MBMs) and governmental incentive policies. Existing studies primarily rely on multi-objective optimization models to achieve a balance between emission reductions and economic benefits. Technological innovation is considered a key pathway to decarbonization, while support from governments and organizations is recognized as crucial for ensuring sustainable development. Future research trends involve leveraging blockchain, big data, and artificial intelligence to optimize and streamline sustainable maritime transport operations, as well as establishing a collaborative governance framework guided by environmental objectives. This study contributes to refining the existing theoretical framework and offers several promising research directions for both academia and industry practitioners. Full article

Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling problem. Effectively managing these issues is essential for optimizing port operations; failure to do so can lead to substantial operational and economic ramifications, ultimately affecting competitiveness within the global shipping industry. Optimization models, encompassing both mathematical frameworks and metaheuristic approaches, offer promising solutions. Additionally, the application of machine learning and reinforcement learning enables real-time solutions, while robust optimization and stochastic models present effective strategies, particularly in scenarios involving uncertainties. This study expands upon earlier foundational analyses of berth allocation, quay crane assignment, and scheduling issues, which have laid the groundwork for port optimization. Recent developments in uncertainty management, automation, real-time decision-making approaches, and environmentally sustainable objectives have prompted this review of the literature from 2015 to 2024, exploring emerging challenges and opportunities in container terminal operations. Recent research has increasingly shifted toward integrated approaches and the utilization of continuous berthing for better wharf utilization. Additionally, emerging trends, such as sustainability and green infrastructure in port operations, and policy trade-offs are gaining traction. In this review, we critically analyze and discuss various aspects, including spatial and temporal attributes, crane handling, sustainability, model formulation, policy trade-offs, solution approaches, and model performance evaluation, drawing on a review of 94 papers published between 2015 and 2024. Full article

To address the issues of path tracking accuracy and control stability in autonomous ship berthing, an improved algorithm combining nonlinear model predictive control (NMPC) and convolutional neural networks (CNNs) is proposed in this paper. A CNN is employed to train on a large dataset of ship berthing trajectories, combined with the rolling optimization mechanism of NMPC. A high-precision path tracking control method is designed, which accounts for ship motion constraints and environmental disturbances. Simulation results show an 88.24% improvement in tracking precision over traditional MPC. This paper proposes an improved nonlinear model predictive control (NMPC) strategy for autonomous ship berthing. By integrating convolutional neural networks (CNNs) and moving horizon estimation (MHE), the method enhances robustness and path-tracking accuracy under environmental disturbances. The amount of system overshoot is reduced, and the anti-interference capability is notably improved. The effectiveness, generalization, and applicability of the proposed algorithm are verified. Full article

Indonesia’s high logistics costs—approximately 14.6% of its GDP—pose a significant challenge to national economic competitiveness. Key contributing factors include complex geography, fragmented multimodal transport systems and inefficient container terminal operations, particularly concerning the handling of empty containers. This study investigates operational optimization in a container terminal using Agile and Lean principles, without additional investment or infrastructure expansion. It compares throughput before and after optimization, focusing on equipment productivity and reduction in idle time, especially related to equipment and human resources. Field implementation began in 2015, followed by simulation-based validation using system dynamics modeling. The terminal demonstrated a sustained increase in capacity beginning in 2016, eventually exceeding its original design capacity while maintaining acceptable berth and Yard Occupancy Ratios (BOR and YOR). Agile practices improved empty container handling, while Lean methods enhanced berthing process efficiency. The findings confirm that significant reductions in port operational costs, shipping operational costs, voyage turnover time, and logistics costs can be achieved through strategic operational reforms and better resource utilization, rather than through capital-intensive expansion. The study provides a replicable model for improving terminal efficiency in ports facing similar constraints. Full article

Autonomous ship berthing requires advanced path planning to balance multiple objectives, such as minimizing berthing time, reducing energy consumption, and ensuring safety under dynamic environmental constraints. However, traditional planning and learning methods often suffer from inefficient search or sparse rewards in such constrained and high-dimensional settings. This study introduces a double deep Q-network (DDQN)-guided dual-population constrained multi-objective evolutionary algorithm (CMOEA) framework for autonomous ship berthing. By integrating deep reinforcement learning (DRL) with CMOEA, the framework employs DDQN to dynamically guide operator selection, enhancing search efficiency and solution diversity. The designed reward function optimizes thrust, time, and heading accuracy while accounting for vessel kinematics, water currents, and obstacles. Simulations on the CSAD vessel model demonstrate that this framework outperforms baseline algorithms such as evolutionary multitasking constrained multi-objective optimization (EMCMO), DQN, Q-learning, and non-dominated sorting genetic algorithm II (NSGA-II), achieving superior efficiency and stability while maintaining the required berthing angle. The framework also exhibits strong adaptability across varying environmental conditions, making it a promising solution for autonomous ship berthing in port environments. Full article

This paper tackles the highly challenging problem of automatic berthing for autonomous surface vessels (ASVs), encompassing trajectory planning, trajectory tracking, and collision avoidance. Firstly, a novel A* algorithm integrated with a quasi-uniform B-spline and quadratic interpolation method (A*QB) is proposed for generating a smooth trajectory from the initial position to the berth, utilizing an offline-generated scaled map. Secondly, the optimal nonlinear model predictive control (NMPC)-based trajectory-tracking framework is established, incorporating the model’s uncertainty, the input saturation, and environmental disturbances, based on a 3-DOF model of a ship. Finally, considering the collision risks during port berthing, a COLREGs-based collision avoidance method is investigated. Consequently, a novel trajectory-tracking and COLREGs-based collision avoidance (TTCCA) scheme is proposed, ensuring that the ASV navigates along the desired trajectory, safely avoids both static and dynamic obstacles, and successfully reaches the berth. To validate the TTCCA approach, numerical simulations are conducted across four scenarios with comparisons to existing methods. The experimental results demonstrate the effectiveness and superiority of the proposed scheme. Full article

Berthing operation heterogeneity across ship types causes significant uncertainty in assessing port congestion and carbon emissions over comparable timeframes. This study quantifies in-port emission dynamics for four cargo ship types (container, liquid bulk, dry bulk, and general cargo) using an operational phase-specific emission accounting model. We propose a hybrid deep learning model that integrates Two-Dimensional Convolutional Neural Networks (2DCNN) with Squeeze-and-Excitation Attention Mechanisms (SEAM) and Bidirectional Long Short-Term Memory Networks (BiLSTM) layers, optimized via the Triangulation Topology Aggregation Optimizer (TTAO) for hyperparameter tuning. Empirical analysis at Ningbo Zhoushan Port shows that liquid bulk carriers emit 23–41% more than other ship types due to extended auxiliary engine/boiler use during cargo handling. The 2DCNN-SEAM model significantly improves BiLSTM prediction accuracy—reducing Mean Absolute Percentage Error (MAPE) by 18.7% and increasing the R² value to 0.94—by effectively capturing spatiotemporal congestion features. Results confirm that operational congestion is a critical emission multiplier, especially for ships requiring prolonged auxiliary system use during berthing. These insights inform targeted decarbonization strategies for port authorities, prioritizing operational efficiency and energy transition for high-emission ship categories. Full article

When vessels are docked at ports, traditional auxiliary engines produce substantial pollutants and noise, exerting pressure on the port environment. Shore power technology, as a green, energy-efficient, and emission-reducing solution, can effectively mitigate ship emissions. However, its widespread adoption is hindered by challenges such as high costs, compatibility issues, and connection complexity. This study develops a multi-objective optimization model for the coordinated allocation of shore power and berth scheduling, integrating economic benefits, environmental benefits, and operational efficiency. The NSGA-III algorithm is employed to solve the model and generate a Pareto-optimal solution set, with the final optimal solution identified using the TOPSIS method. The results demonstrate that the optimized shore power distribution and berth scheduling strategy can significantly reduce ship emissions and port operating costs while enhancing overall port resource utilization efficiency. Additionally, an economically feasible shore power allocation scheme, based on 80% of berth capacity, is proposed. By accounting for variations in ship types, this study provides more targeted and practical optimization strategies. These findings offer valuable decision support for port management and contribute to the intelligent and sustainable development of green ports. Full article

Shore power represents one of the principal solutions for the green transformation within the port industry. It significantly aids in the reduction in carbon emissions from vessels while they are berthed in port, yet often necessitates an effective promotional strategy to foster its installation and utilization. Stakeholders including port authorities, ship operators, and local governments all play a crucial role in achieving this objective. This paper employs a tripartite evolutionary game model in conjunction with a system dynamics model to investigate the evolutionary responses of stakeholders when policy tools are applied, and consequently, to elucidate the dynamics of strategy effectiveness. In this context, six business scenarios are developed to ascertain the potential impacts of implementing subsidies and carbon taxes. The findings demonstrate that any singular strategy, whether a subsidy or a carbon tax, is inadequate for the successful advancement of shore power; on the contrary, a government-led, integrated, and dynamic reward–punishment strategy aids in stabilizing the inherent fluctuations within this game process. Moreover, the initial willingness of ship operators exerts a considerably greater influence than that of the other two stakeholders. Full article

In some ports, there are separate very narrow places between the quays and other navigational obstacles, where the distance between the quays or between the quays and navigational obstacles is very small. Narrow gaps or channels in the water area, where quays are built and ships are berthing, make it difficult for ships to berth at such quays. Accurate knowledge of a ship’s manoeuvrability characteristics, combined with the application of these characteristics in berthing operations and the optimal use of tugboat capabilities, allows for better utilization of restricted port spaces. The article presents a developed ship berthing methodology designed for ship berthing in extremely limited conditions, utilizing the ship’s manoeuvrability capabilities and maximizing the capabilities of tugboats when mooring ships in extremely limited conditions. The developed methodology was tested with real ships and tugboats in specific port conditions and using calibrated simulators, and the results of the experimental research and theoretical calculations are presented in the article as a case study. The research results (methodology) obtained and presented in the article can be applied to any ships and ports, precisely adapting them to specific port situations. The article studies ship manoeuvrability and tugboat capabilities under various hydrometeorological and hydrological conditions, assesses the impact of shallow depths (shallowness), and determines the boundary conditions for ship berthing. Full article

Maritime transportation significantly contributes to air pollution, especially in coastal cities. Air pollution represents the greatest health risk related to the environment in the European Union. Therefore, the European Commission published the European Green Deal, which introduces the rule of zero-emission requirements for ships at berths with the mandatory use of power supply from shore or alternative technologies without emissions. The electrification of ferries has proven to be a key approach in reducing the negative impact on the environment; hence, it is necessary to provide adequate infrastructure for charging electric ferries. To determine the energy needs of the shore connection, a daily energy profile of the ferry fleet was created. Due to the sailing schedule, daily energy needs may be non-periodic. By optimizing the charging process, a reduction in peak charging power can be achieved. The charging process was optimized using particle swarm optimization. To improve the function goal, the parameters of the model were analyzed and optimized. It was found that the correct selection of population size and inertia weight factor can significantly enhance the optimization effect. The proposed model can be applied to other ports of interest, considering the specifics of the exploitation of the fleet of ships. Full article

The essence of this work is to explore the potential of shore-side power as a sustainable and efficient solution for mitigating greenhouse gas emissions from passenger ships during their berthing periods in ports. Cruise ships—including both cruise liners and ferries—are significant contributors to port-based emissions because they rely on onboard auxiliary engines to generate power while docked. This practice results in the continuous release of greenhouse gases, such as carbon dioxide, and other pollutants, including nitrogen oxides and sulfur oxides, which contribute to environmental degradation and pose public health concerns in port cities. The objective of this study is to highlight the critical role of shore-side power in decarbonizing the maritime industry and in helping achieve global climate targets. By addressing environmental, technical, and economic aspects, the research aims to provide a comprehensive framework for decision-makers, port authorities, and shipping companies to adopt this technology as a key measure for reducing emissions from cruise ships. Furthermore, this study investigates the multifaceted impacts of shore-side electricity—focusing on its long-term environmental, economic, and social implications—by analyzing case studies from ports that have successfully implemented this technology and by examining the barriers to its broader adoption. Ultimately, this research seeks to provide actionable insights for policymakers, port authorities, and shipping companies. Full article

This study deals with berth allocation for Maritime Autonomous Surface Ships (MASSs) in the context of the mixed operation of MASSs and manned vessels from the perspective of port-shipping companies’ collaboration. Two berth allocation strategies, namely the separated-type and the mixed-type, are proposed in this article. Two mixed integer nonlinear programming models aimed at minimizing the total docking cost of the vessels in the port and the waiting time for berths are developed and solved using Gurobi, respectively. A large-scale simulation of the mixed-type berth allocation model is carried out using an improved simulated annealing algorithm. Several experiments are conducted to test the effectiveness of the model and to draw insights for commercializing autonomous vessels. The presented results show that multi-objective modeling and optimization should be conducted from the collaboration of port-shipping companies, which is more efficient from the perspective of shipping companies or ports, respectively. When berth resources are limited or there is a high requirement for operational safety, the separated-type berth allocation strategy is more efficient. When the number of MASS-dedicated berths reaches a certain proportion, the total docking cost of the vessel no longer changes, indicating that more dedicated berths are not better. Full article

This study investigates the spatiotemporal density aggregation and pattern distribution of vessel traffic amidst bustling maritime logistics scenarios. Firstly, a relatively new spatiotemporal segmentation and reconstruction method is proposed for ship AIS trajectories to address trajectory disruptions caused by berthing, anchorage, and other factors. Subsequently, a trajectory filtering algorithm utilizing time window panning is introduced to mitigate position jumps and deviation errors in trajectory points, ensuring that the dynamic trajectory adheres to the spatiotemporal correlations of ship motion. Secondly, to establish a geographical spatial mapping of dynamic trajectories, spatial gridding is applied to maritime traffic areas. By associating the geographical space of traffic activities with the temporal attributes of dynamic trajectories, a dynamic trajectory temporal density model is constructed. Finally, a case study is conducted to evaluate the effectiveness and applicability of the proposed method in identifying spatiotemporal patterns of maritime traffic and spatiotemporal density aggregation states. The results show that the proposed method can identify dynamic trajectory traffic patterns after the application of compression algorithms, providing a novel approach to studying the spatiotemporal aggregation of maritime traffic in the era of big data. Full article