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Recent maritime cybersecurity accidents reveal that shipping is facing increased exposure to cyber threats, especially due to the fast-growing digitalisation of the sector, leaving vessels and their onboard systems vulnerable to cyberattacks. This research aims at evaluating the relationship among the critical dimensions influencing cybersecurity performance in the maritime industry. To achieve this, six critical dimensions related to cybersecurity preparedness are first identified through literature review, namely 'regulations', 'company procedures' from a managerial perspective and 'shipboard systems readiness', 'training and awareness', 'human factor' and 'compliance monitoring' at an operation level. A Likert-scale questionnaire is designed and used to collect empirical data from 133 seafarers and shore-based staff. Structural Equation Modelling (SEM) is applied to examine the causal relationships between the six dimensions and shipboard cybersecurity performance. The results show that 'regulations' positively influence shipping companies' cybersecurity-related 'procedures', which in turn positively affects 'shipboard systems readiness', 'training and awareness', and 'monitoring'. Further, 'training and awareness' positively influences the cybersecurity performance of ships. The results have profound implications for the shipping industry on how to strengthen their cyber practices in order to improve their cybersecurity performance. Recommendations for future academic research related to maritime cybersecurity are also provided. [ABSTRACT FROM AUTHOR]

Maritime autonomous surface ships (MASS) promise enhanced efficiency, reduced human errors, and to improve maritime traffic safety. However, MASS navigation in complex maritime traffic presents challenges, especially in collision avoidance strategy optimization (CASO). This paper proposes a novel risk-based CASO approach based on safe reinforcement learning (SRL) with a reliability and risk hierarchical critic network (SRL-R2HCN) approach. Key steps in developing the approach start with the formulation of collision risk assessment. This is followed by the construction of a hierarchical network structure, supplemented by the supporting reward function, multi-objective function, and reliability measurement to realize the SRL-R2HCN. Finally, simulation experiments are conducted in mixed obstacle scenarios, and the results are compared with traditional algorithms to showcase the advancement and fidelity of the new SRL-R2HCN method. The results demonstrate that the proposed method can accurately assess collision risks in mixed obstacle scenarios and generate safe, efficient, and reliable collision avoidance strategies. The outcomes of this research provide a sound theoretical basis for the future development of MASS navigation safety and significant potential to improve the safe and efficient operations of MASS. Furthermore, the methodology could also benefit maritime transportation and shipping management. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has significantly affected the cruise shipping industry, disrupting ports and shipping. However, current research predominantly focuses on the impact on individual ports or vessels, leaving a gap in understanding how these disruptions propagate across cruise shipping networks. To address this gap, a novel vulnerability assessment methodology that offers a comprehensive perspective on the broader impact of COVID-19 on cruise shipping networks is developed. It first uses a new weight social network analysis approach to quantify the vulnerability of each cruise port in a shipping network and then combines the curie port local pandemic risk to generate a new index to reveal the COVID-19 impact on the whole cruise shipping network systematically. The new methodology is applied to analyse the ASEAN cruise shipping network. This real-world COVID-19 pandemic case study yields valuable insights that bridge theoretical and practical domains. Integrating local port-level vulnerabilities with shipping network-level vulnerabilities creates a unique index. This index quantifies the individual and collective influence of COVID-19 risks at different cruise ports on the entire regional cruise shipping network. The results directly impact cruise lines seeking to enhance their operations' resilience in the face of COVID-19 challenges. The vulnerability index explains how risk exposure at various ports shapes the network's dynamics. This insight empowers cruise lines to optimise ship deployment schedules, lowering the network's overall COVID-19 pandemic risk. The research method and outcomes offer a pioneering perspective on the vulnerability of cruise shipping networks to COVID-19 disruptions, and other possible disruptions (e.g., climate change) in a broad sense. By elucidating interconnected vulnerabilities, cruise lines are equipped with actionable insights to navigate the complexities of global challenges. [ABSTRACT FROM AUTHOR]

This article collectively addresses the issues of optimizing the sailing speed of containerships and fleet deployment in intercontinental container liner shipping, taking cargo time values into consideration. A mixed-integer nonlinear programming model is developed to maximize the gross profits of a container shipping line. The sailing speed is divided into 0.1 knot intervals and a reciprocal-discretization method is proposed to transform the model into an integer-based linear programming model, which can be solved using optimization solvers such as CPLEX. A case study is conducted based on the Far East-West America liner route of a global container shipping line to assess the effectiveness of the proposed model and the reciprocal-discretization method. The results show that when the cargo time value is taken into consideration, the increased sailing speed of containerships on long legs and the decreased number of deployed containerships will generate greater operating profits for the container shipping line. The rise in bunker price will lead to a stepwise decline in sailing speed of containerships and a stepwise growth in the number of deployed containerships. Moreover, the downward trend of sailing speed due to the increase of bunker price will be somewhat delayed as the unit container cargo value increases. Several useful insights are drawn from analysis of the results. [ABSTRACT FROM AUTHOR]

Highlights • Analysis of accident severity in waterborne transportation. • Collection and statistical analysis of maritime accident data over 30 years. • Identification of key factors influencing maritime accident severity using data-driven BN and expert verification. • Dynamic prediction of maritime accident severity under high uncertainties. Abstract The rapid development of the shipping industry requires the use of large vessels carrying high-volume cargoes. Accidents incurred by these vessels can lead to a heavy loss of life and damage to the environment and property. As a leading country in international trade, China has developed its waterway transport systems, including inland waterways and coastal shipping, in the past decades. A few catastrophic shipping accidents have occurred during this period. This paper aims to develop a new risk analysis approach based on Bayesian networks (BNs) to enable the analysis of accident severity in waterborne transportation. Although the risk data are derived from accidents that occurred in China's waters, the risk factors influencing accident severity and the risk modelling methodology are generic and capable of generating useful insights on waterway risk analysis in a broad sense. To develop the BN-based risk model, waterway accident data are first collected from all accident investigation reports by China's Maritime Safety Administration (MSA) from 1979 to 2015. Based on the derived quantitative data, we identify the factors related to the severity of waterway accidents and use them as nodes of the risk model. Second, based on a receiver operating characteristic (ROC) curve, an augmented naïve BN (ABN) model is selected through a comparative study with a naïve BN (NBN) model to analyse the key risk factors influencing waterway accident severity. The results show that the key factors influencing waterway safety include the type and location of the accident and the type and age of the ship. Moreover, a novel scenario analysis is conducted to predict accident severity in various situations by combining different states (e.g., high risk) of the key factors to generate useful insights for accident prevention. More specifically, the findings can aid transport authorities, ship owners and other stakeholders in improving waterborne transportation safety under uncertainty. [ABSTRACT FROM AUTHOR]

In the past decades, maritime transportation not only contributes to economic prosperity, but also renders many threats to the industry, causing huge casualties and losses. As a result, various maritime safety measures have been developed, including Port State Control (PSC) inspections. In this paper, we propose a data-driven Bayesian Network (BN) based approach to analyse risk factors influencing PSC inspections, and predict the probability of vessel detention. To do so, inspection data of bulk carriers in seven major European countries from 2005 to 2008 1 In 2008, New Inspection Regime (NIR) was first introduced in Paris MoU port state control. Two sets of data, before and after 2008 are being collected for analysis of the effect of NIR. This paper, as the first phase study, analyses the detention probability before the implementation of NIR. 1 in Paris MoU is collected to identify the relevant risk factors. Meanwhile, the network structure is constructed via TAN learning and subsequently validated by sensitivity analysis. The results reveal two conclusions: first, the key risk factors influencing PSC inspections include number of deficiencies, type of inspection, Recognised Organisation (RO) and vessel age. Second, the model exploits a novel way to predict the detention probabilities under different situations, which effectively help port authorities to rationalise their inspection regulations as well as allocation of the resources. Further effort will be made to conduct contrastive analysis between ‘Pre-NIR’ period and ‘Post-NIR’ period to test the impact of NIR started in 2008. [ABSTRACT FROM AUTHOR]

This paper investigates the attractiveness of ports in West Africa through the development of a container shipping lines' port choice methodology. Although many multi-criteria decision-making methods have been developed and applied to facilitate a rational port choice process, few have investigated the criteria used by shipping lines when selecting ports in West Africa. With the rapid economic development of West Africa, the task of establishing a rational model to guide shipping lines to choose their favourite ports in the region becomes urgent. In this work, 16 criteria are identified to assist shipping lines in port choice from four perspectives including adequate infrastructure, port location, port charge, and port administration/port efficiency. In order to quantitatively evaluate these criteria, an analytical hierarchy process approach is used to make use of subjective judgements to compensate the incompleteness of objective data. One of the important findings from this study is that port infrastructure is the most crucial criterion in terms of the port attractiveness in West Africa. It is followed by port draught, political stability, market size/cargo volume, and international networks. The research outcomes also indicate that the port of Abidjan is the most attractive container port in West Africa, followed by Dakar when all the identified important criteria are taken into account. [ABSTRACT FROM AUTHOR]

This paper studies the contributing factors of maritime piracy by analyzing previous incidents that have been reported to the International Maritime Organization (IMO). Part of the analysis is to filter those ship types that are particularly vulnerable to piracy attacks. The paper also introduces the guidelines developed by the IMO and the industry envisaging to minimize the risk to ships that are exposed to attacks from pirates. It further describes the initiatives taken to develop a sustainable mechanism in the high-risk area (HRA)1to suppress piracy and other maritime crimes. This study reflects the fact that maritime security and piracy issues’ importance has been increasingly recognized in the 40 year history ofMaritime Policy & Management. [ABSTRACT FROM PUBLISHER]

Rapid development of maritime transportation networks meets international trade demands while rendering them in high risk and disruption concerns particularly at ports being the bottlenecks of the whole flows. Port operations calls for an effective approach to assess ports vulnerability and to ensure the resilience of their associated maritime supply chains (MSC). However, traditional quantitative risk analysis reveals challenges due to data incompleteness and ambiguity, and operational and environmental uncertainty when being applied in ports vulnerability analysis. This paper aims to develop a novel port vulnerability assessment (PVA) framework, which can guide and realise a standardised vulnerability analysis process for the ports from different geographies involving in the same MSC and hence the resources can be better managed from a global network level for optimal resilience of the chain. It is especially important for the shipping and port industries which are in nature international and desires strong international uniform standardization. The fuzzy theory, evidential reasoning (ER) approach, and expected utility theory are combined in a holistic way to form the proposed PVA framework. The new framework is validated and demonstrated by using a case study in which five key ports along an established MSC in China are investigated. The findings can be used as a stand along method to compare the vulnerability levels of the ports in an MSC and/or integrated with decision optimisation methods for rational safety resource distribution from a supply chain perspective. • Use of a hybrid of the fuzzy theory and ER methods for port vulnerability assessment from a supply chain perspective. • Provide a standardised process and a novel PVA framework incorporating a centrality estimate for the first time. • Define different types of vulnerability measures considering the internal risk and external safety impact in the framework. • Investigate five key ports to analyse the vulnerability and prioritise their vulnerable levels for resource rationalisation. [ABSTRACT FROM AUTHOR]

• Provide an in-depth evolution analysis in the practical development of shore power. • Conduct a systematic bibliometric analysis of pivotal shore power-related publications. • Coordinate practical shore power applications with academic research to highlight the predominant trends. • Divide shore power research into four categories to understand its progress and trends. • Establish well-reasoned remarks, implications, and agenda for future studies. Shore power not only reduces ship emissions and noise in berthing but also has shown practical implications for maritime competitiveness. However, the existing literature and industry reports reveal that a limited number of ships have access to shore power. This highlights the need for research into the constraints faced by the sector and the development of solutions to enhance its adoption. Addressing these issues will enable the port and shipping industries to enjoy the associated benefits. Currently, existing research on shore power remains dispersed, yet it fully addresses such questions and lacks an integrated framework, making it challenging to extract pivotal insights. This paper aims to conduct a state-of-the-art review of shore power by the Web of Science Core Collection and Scopus databases as a pivotal solution to shipping emissions reduction in ports and advancing the maritime sector towards carbon neutrality. This analysis is grounded in a bibliometric analysis of existing literature on this topic, with a focus on the need to devise robust strategies to fully harness the potential of shore power for carbon neutrality. The valuable findings are revealed, including (i) The adoption of shore power by ports is predominantly propelled by regulatory mandates and incentives, inclusive of government subsidies in leading regions such as China, the U.S., and Europe; (ii) Due to the implementation of Emission Control Areas and carbon neutrality regulations, an increasing number of port operators in Europe are turning to shore power to fulfil both the governmental or industrial requirements, especially regarding cruise ships; (iii) The literature on shore power primarily delves into four main research areas: the inherent features of shore power, emission inventory assessments, practical applications of shore power, and energy management strategies; and (iv) Emerging directions in shore power research include cooperative optimisation among stakeholders, integration of new technologies into shore power, a holistic evaluation of the multifaceted advantages of diverse emission reduction strategies, and critical examinations of any unintended consequences stemming from shore power adoption. They offer invaluable insights on enhancing the adaptation and effectiveness of shore power. [ABSTRACT FROM AUTHOR]

• Conduct the state-of-the-art survey to generate ten advanced prediction methods. • Carry out the comprehensive experimental comparison and deep analysis. • Develop a benchmark for trajectory prediction methods in maritime traffic. • Employ six evaluation indexes to assess the performance of ten prediction methods. • Evaluate the fitness of the ten prediction methods against different scenarios. • Validate their performance in two complex water areas in different scenarios. Maritime transport faces new safety challenges in an increasingly complex traffic environment caused by large-scale and high-speed ships, particularly with the introduction of intelligent and autonomous ships. It is evident that Automatic Identification System (AIS) data-driven ship trajectory prediction can effectively aid in identifying abnormal ship behaviours and reducing maritime risks such as collision, stranding, and contact. Furthermore, trajectory prediction is widely recognised as one of the critical technologies for realising safe autonomous navigation. The prediction methods and their performance are the key factors for future safe and automatic shipping. Currently, ship trajectory prediction lacks the real performance measurement and analysis of different algorithms, including classical machine learning and emerging deep learning methods. This paper aims to systematically analyse the performance of ship trajectory prediction methods and pioneer experimental tests to reveal their advantages and disadvantages as well as fitness in different scenarios involving complicated systems. To do so, five machine learning methods (i.e., Kalman Filter (KF), Support Vector Progression (SVR), Back Propagation network (BP), Gaussian Process Regression (GPR), and Random Forest (RF)) and seven deep learning methods (i.e., Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (Bi-LSTM), Sequence to Sequence (Seq2seq), Bi-directional Gate Recurrent Unit (Bi-GRU), and Transformer) are first extracted from the state-of-the-art literature review and then employed to implement the trajectory prediction and compare their prediction performance in the real world. Three AIS datasets are collected from the waters of representative traffic features, including a normal channel (i.e., the Chengshan Jiao Promontory), complex traffic (i.e., the Zhoushan Archipelago), and a port area (i.e., Caofeidian port). They are selected to test and analyse the performance of all twelve methods based on six evaluation indexes and explore the characteristics and effectiveness of the twelve trajectory prediction methods in detail. The experimental results provide a novel perspective, comparison, and benchmark for ship trajectory prediction research, which not only demonstrates the fitness of each method in different maritime traffic scenarios, but also makes significant contributions to maritime safety and autonomous shipping development. [ABSTRACT FROM AUTHOR]

• Analyse the risks of maritime supply chains from different perspectives. • Develop an advanced risk analysis method to tackle the uncertainty in risk data. • Compare risk analysis results by using different methods to demonstrate the advantages of the newly proposed one. • Conduct an empirical study to provide useful insights for the identification and control of high risks. This paper aims to develop a novel model to assess the risk factors of maritime supply chains by incorporating a fuzzy belief rule approach with Bayesian networks. The new model, compared to traditional risk analysis methods, has the capability of improving result accuracy under a high uncertainty in risk data. A real case of a world leading container shipping company is investigated, and the research results reveal that among the most significant risk factors are transportation of dangerous goods, fluctuation of fuel price, fierce competition, unattractive markets, and change of exchange rates in sequence. Such findings will provide useful insights for accident prevention. [ABSTRACT FROM AUTHOR]

Environmental problems that seriously affect both natural systems and social development of human beings have drawn extensive attention from governing authorities all around the world, and become an urgent issue to be addressed. Ports play a significant role in the international shipping which inevitably influence the global environment. Thus, the concept of green port is developed to mitigate the negative impacts of inappropriate port operations on environment. This paper analyzes the current status of green port development worldwide. An evaluation model for quantitative measurement of green port development is established based on the Drivers, Pressures, States, Impacts and Responses (DPSIR) framework. The weight of each index composing the evaluation model is calculated through an analytical hierarchy process method, and the evaluation results of the investigated ports with respect to each index are aggregated using an evidential reasoning approach. The evaluation model is further demonstrated through a comparative analysis of five major ports in China. The novel model developed along with the methods applied in this paper can provide significant insights for the comparative evaluation on the development of green ports in other countries and/or regions, as well as a powerful tool to conduct self-assessment of green port development. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the different concepts of “vulnerability” used in maritime supply chains, and to develop a novel framework with supporting models to identify and analyze the relevant vulnerabilities in the chains. A real case of the Maersk shipping line in its Asia-Europe route is studied to demonstrate the applicability of the proposed framework. We find that the investigated network has stronger robustness against random failures than that when facing deliberate attacks. Furthermore, to identify vulnerable nodes (i.e. ports) of the network, two different types of analysis are undertaken through a multi-centrality model and a robustness analysis model, respectively. Consequently, the vulnerabilities estimated through robustness analysis can ascertain those by the classical centrality methods when they appear on both analysis results. More importantly, the similarity between the two outcomes can help gain more confidence on the accuracy in terms of the identification of the vulnerabilities in the system, while the difference (if any) such as those identified by the robustness analysis but not by the centrality analysis (or vice versa) can trigger a further investigation to find the comprehensive vulnerable nodes against different threats/hazards. It will aid rational decision on design and operation of resilient and robust maritime supply chains. [ABSTRACT FROM AUTHOR]

• A new method to identify hazards and quantify the associated risks of HEPS. • A new cruise ship evacuation risk assessment model combining FMEA, BBN, ER approaches holistically. • New investigation on the incorporation of a HSEO framework into the risk assessment of HEPS. • Real case study and analysis of a large-scale evacuation drill of a cruise ship. In maritime transport, evacuation, escape and rescue play a crucial role in protecting people's lives when a passenger ship is involved in a serious accident. The study aims to develop a new method to identify hazards, quantify and rank the associated risks in the process of Human Evacuation from Passenger Ships (HEPS). Firstly, based on extensive literature review and marine accident investigation reports, the risk factors affecting passenger ship evacuation were analysed and identified, and an analysis framework based on Human, Ship, Environment and Organization (HSEO) for HEPS was proposed. Secondly, a risk assessment model was proposed to quantify and rank risk factors in the process of HEPS. Finally, a large-scale evacuation drill of a cruise ship was taken as a case study to demonstrate the applicability of the proposed evaluation model, and accuracy of the results. The results reveal that (1) evacuation decision, operation of Life-Saving Appliances (LSAs) are the main risks affecting the safety of HEPS; (2) the behaviours of passengers have a relatively lower risk priority; and (3) future HEPS research should focus on the development of a multi-attribute decision system to address the issue on when to evacuate and when to abandon a ship. [ABSTRACT FROM AUTHOR]

Shipping industry continues to support the development of world trade and globalization of logistics network. Maritime safety has a direct bearing on human life, property and health of the marine environment, therefore, safety has always been the focus of maritime transportation. Total-loss marine accidents present the most serious accident type, which is therefore crucial for decision-making and guidance on rational safety resource allocation to analyze and evaluate its influential factors. This paper selects the data on the total-loss marine accidents that occurred in the world from 1998 to 2018, involving 16 ship types and 13 main navigation sea regions. Based on an improved entropy weight-TOPSIS model, the results show that in the both models with respect to ship type and sea region, the main influential factors are foundering, stranding and fires/explosions. This paper aims to aid the relevant maritime safety authorities such as the International Maritime Organization and ship owners/operators to take effective risk control actions to avoid/reduce the occurrence of total-loss marine accidents in future. • Total-loss accident governance is very important for ocean and maritime safety. • Factors influencing total-loss accidents are explored. • An entropy weight-TOPSIS model for total-loss accident evaluation is developed. • Historical data analysis provides decision-making basis for stakeholders. [ABSTRACT FROM AUTHOR]