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Climate extremes are threatening transportation infrastructures and hence require new methods to address their vulnerability and improve their resilience. However, existing studies have yet to examine the climate impacts on transportation networks systematically rather than independently assessing the infrastructures at a component level. Therefore, it is crucial to configure alternative shipping routes from a systematic perspective to reduce climate vulnerabilities and optimise the resilience of the whole shipping network. This paper aims to assess the global shipping network focusing on climate resilience by a methodology that combines climate risk indicators, centrality analysis and ship routing optimisation. The methodology is designed for overviewing the climate vulnerability of the current and future scenarios for comparison. First, a multi-centrality assessment defines the global shipping hubs and network vulnerabilities. Secondly, a shipping model is built for finding the optimal shipping route between ports, considering the port disruption days caused by climate change (e.g. extreme weather) based on the climate vulnerability analysis result from the first step. It contributes to a new framework combining the global and local seaport climate vulnerabilities. Furthermore, it recommends changing shipping routes by a foreseeable increase in port disruptions caused by extreme weather for climate adaptation. [ABSTRACT FROM AUTHOR]

The widespread interest in using drones in maritime transport has rapidly grown alongside the development of unmanned ships and drones. To stimulate growth and address the associated technical challenges, this paper systematically reviews the relevant research progress, classification, applications, technical challenges, and possible solutions related to the use of drones in the maritime sector. The findings provide an overview of the state of the art of the applications of drones in the maritime industry over the past 20 years and identify the existing problems and bottlenecks in this field. A new classification scheme is established based on their flight characteristics to aid in distinguishing drones' applications in maritime transport. Further, this paper discusses the specific use cases and technical aspects of drones in maritime rescue, safety, navigation, environment, communication, and other aspects, providing in-depth guidance on the future development of different mainstream applications. Lastly, the challenges facing drones in these applications are identified, and the corresponding solutions are proposed to address them. This research offers pivotal insights and pertinent knowledge beneficial to various entities such as maritime regulatory bodies, shipping firms, academic institutions, and enterprises engaged in drone production. This paper makes new contributions in terms of the comprehensive analysis and discussion of the application of drones in maritime transport and the provision of guidance and support for promoting their further development and integration with intelligent transport. [ABSTRACT FROM AUTHOR]

21st Century Maritime Silk Road (MSR) is of significant importance for world freight transport. The ports along the MSR present a key element of the involved shipping networks to support the connectivity of the MSR. Therefore, it is crucial to carry an effective safety assessment of the ports to ensure the robustness and sustainability of the growing MSR. However, traditional quantitative risk analysis approaches (QRA) used in ports face many challenges when being applied within the context of the MSR, such as risk data incompleteness and ambiguity, and operational and environmental uncertainties. This paper proposes a novel safety evaluation approach to address these issues encountered during the risk analysis process in the MSR ports. The fuzzy set theory (FST), an evidential reasoning (ER) approach, and the expected utility theory are integrated in a holistic way in the proposed methodology. The proposed methodology is used to analyse five key ports along the MSR. The results provide decision-makers with useful insights on enhancing port safety, effective route planning as well as improving operational efficiency. [ABSTRACT FROM AUTHOR]

• Utilisation of historical records and machine learning to generate a human fatigue model. • Identification of significant factors from an objective accident/incident occurrence perspective. • Combination of LASSO and BN to propose a data-driven model to investigate human fatigue. • Providing solution to fatigue research with limited psychological data. In maritime transport, fatigue conditions can impair seafarer performance, pose a high risk of maritime incidents, and affect safety at sea. However, investigating human fatigue and its impact on maritime safety is challenging due to limited objective measures and little interaction with other risk influential factors (RIFs). This study aims to develop a novel model enabling accident data-driven fatigue investigation and RIF analysis using machine learning. It makes new methodological contributions, such as 1) the development of a human fatigue investigation model to identify significant RIFs leading to human fatigue based on historical accident and incident data; 2) the combination of least absolute shrinkage and selection operator (LASSO) and bayesian network (BN) to formulate a new machine learning model to rationalise the investigation of human fatigue in maritime accidents and incidents; 3) provision of insightful implications to guide the survey of fatigue's contribution to maritime accidents and incidents without the support of psychological data. The results show the importance of RIFs and their interdependencies for human fatigue in maritime accidents. It takes advantage of available knowledge and machine learning to open a new direction for fatigue management, which will benefit the maritime fatigue investigation and provide insights into other high-risk sectors suffering from human fatigue (e.g. nuclear and offshore). [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]

• Holistic use of fNIRS and maritime simulation to conduct HPM objectively. • Development of a hybrid assessment model using haemoglobin data and ANN. • Pioneering psychophysiological data-driven machine learning for seafarers' HPM. • Real case analysis for classifying seafarers of different qualifications. Human errors significantly contribute to transport accidents. Human performance measurement (HPM) is crucial to ensure human reliability and reduce human errors. However, how to address and reduce the subjective bias introduced by assessors in HPM and seafarer certification remains a key research challenge. This paper aims to develop a new psychophysiological data-driven machine learning method to realize the effective HPM in the maritime sector. It conducts experiments using a functional Near-Infrared Spectroscopy (fNIRS) technology and compares the performance of two groups in a maritime case (i.e. experienced and inexperienced seafarers in terms of different qualifications by certificates), via an Artificial Neural Network (ANN) model. The results have generated insightful implications and new contributions, including (1) the introduction of an objective criterion for assessors to monitor, assess, and support seafarer training and certification for maritime authorities; (2) the quantification of human response under specific missions, which serves as an index for a shipping company to evaluate seafarer reliability; (3) a supportive tool to evaluate human performance in complex emerging systems (e.g. Maritime Autonomous Surface Ship (MASS)) design for ship manufactures and shipbuilders. [ABSTRACT FROM AUTHOR]

• Time series forecasting is conducted to unveil ship emissions' dynamics. • A ship navigation identification algorithm is proposed by using AIS data. • A modular deep learning model is developed for precise time series forecasting. • A deep learning model is developed to resist concept drift caused by emergency events. • The proposed model is interpreted via data enhancement and global sensitivity experiments. Accurate prediction of ship emissions aids to ensure maritime sustainability but encounters challenges, such as the absence of high-precision and high-resolution databases, complex nonlinear relationships, and vulnerability to emergency events. This study addresses these issues by developing novel solutions: a novel Spatiotemporal Trajectory Search Algorithm (STSA) based on Automatic Identification System (AIS) data; a rolling structure-based Seasonal-Trend decomposition based on the Loess technique (STL); a modular deep learning model based on Structured Components, stacked-Long short-term memory, Convolutional neural networks and Comprehensive forecasting module (SCLCC). Based on these solutions, a case study using pre and post-COVID-19 AIS data demonstrates model reliability and the pandemic's impact on ship emissions. Numerical experiments reveal that the STSA algorithm significantly outperforms the conventional identification standard in terms of accuracy of ship navigation state identification; the SCLCC model exhibits greater resistance against emergency events and excels in comprehensively capturing global information, thus yielding higher accurate prediction results. This study sheds light on the changing dynamics of maritime transport and its impacts on carbon emissions. [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]

Highlights • The study compared the current status of LNG fuelled ships between China and European countries. • A novel model for evaluating the development of LNG Fuelled Ships is proposed. • Sensitive analysis is conducted to test the soundness and robustness of the model. • Norway performances better than USA and China in terms of the development level of LNG fuelled ships. Abstract In recent years, increasingly strict restrictions on ship emissions and continuously increasing prices of marine fuel oil have made the liquefied natural gas (LNG) using as a marine fuel more attractive, and LNG fuelled ships have therefore become more popular in many countries. However, there is still not much research on the development level of LNG fuelled ships in different countries, and no unified or corresponding evaluation criteria has been established to support relevant policy making, revealing a significant research gap to be fulfilled. In view of this, taking the advantages of the PEST (Political, Economic, Social and Technological factors) and the SWOT (strengths, weaknesses, opportunities, and threats) analysis, this paper proposes a novel SRETI (Strategy, Regulation, Economics, Technology and Infrastructure) model for evaluating the development level of LNG fuelled ships in a particular region or country for self-assessment or comparative studies. The kernel of the model consists of the combination of the analytic hierarchy process (AHP) method and the evidential reasoning (ER) approach, thus being able to deal with evaluation data of both quantitative and qualitative features. China, Norway and the United States of America (USA) are selected in a real case study to demonstrate the feasibility of the model on the evaluation of the development of their LNG fuelled ships. The findings show that Norway is better than USA and China in terms of the development level of LNG fuelled ships. It is also revealed that the proposed SRETI model is capable of addressing uncertainties in subjective data provided by domain experts. A sensitive analysis is conducted as well to test the robustness of the SRETI model, and the results are in harmony with the axioms and hypotheses. This work provides policymakers with powerful insights into the development of LNG fuelled ships. It can also be tailored to evaluate the development of emerging technologies in other sectors. [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]

There are many factors affecting navigational safety in ports, including weather, the characteristics of the channels and vessel types, etc. This paper aims to identify the factors influencing navigational safety in ports and to analyze the extent to which such factors affect the safety of ports from the perspective of ship captains through a real case study. A quantitative analysis is carried out using the data collected from 21 captains who have over 10 years experience in operating ships individually. The identified factors indicate risk implications in ports. A fuzzy analytical hierarchy process is used to evaluate the importance of the factors and to rank the safety levels of the targeted ports in Korea from a captain’s perspective. Consequently, among Busan, Ulsan, Gwangyang, Incheon, and Mokpo, Busan is evaluated by captains as the safest port, while Mokpo is the most risky. The research also reveals that it is applicable to use domain expert knowledge when historical failure data is unavailable or difficult to access to evaluate port safety. The result shows great research significance in terms of providing relevant stakeholders, such as port authorities and shipping companies, with an insight into port safety performance and thus facilitating the development of the associated risk control measures. [ABSTRACT FROM AUTHOR]

• Incorporate ship deficiency data into port state control analysis (PSC). • Develop a bi-directional risk analysis tool to predict ships' detention likelihood and diagnose the most likely reason for the occurrence of ship deficiency. • Use a BN-based dynamic model to prioritize the impact of factors influencing PSC inspection. • Analyze the dependency and interdependency among the factors influencing PSC inspection. • Conduct an empirical study in the Tokyo MoU region to provide useful insights for rational risk based port state control. Port State Control (PSC) inspection aids to control substandard ships and ensure safety at sea. Current risk-based PSC research and practice fail to incorporate ship deficiency records into detention probability analysis, because of the difficulty introduced by the involved big deficiency data. In this paper, a new Bayesian Network (BN) based PSC risk probabilistic model is developed to analyze the dependency and interdependency among the risk factors influencing PSC inspections based on big data derived from the inspection database of Tokyo MoU for the period between 2014 and 2017. The results reveal that ship's safety condition related deficiencies as well as technical features of the inspected vessel itself are among the most influential factors concerning PSC inspections and ship detention. New Bayesian learning methods are used to improve the model efficiency in ship detention prediction. As a result, the newly developed model has shown a reliable performance on dynamic prediction and cause-effect diagnosis of ship detention probabilities by pioneering the incorporation of ship deficiency records in the analysis. The findings provide important insights on how to facilitate risk-based PSC inspections for both ship owners and port states. They provide support for port state authorities to implement rational inspection policies. [ABSTRACT FROM AUTHOR]

• Incorporate economy and efficiency factors into safety based cargo pre-stowage planning of general cargo ships. • Develop a new preconditioning evidential reasoning to aid rational cargo pre-stowage plans and decision. • Propose a new model for dynamic decision criterion weights using entropy and AHP. • Conduct an empirical study to provide useful insights for rational cargo pre-stowage of general cargo ships. The current approach to the cargo stowage plans (CSP) of general cargo ships (GCS) is safety-driven, which means that any CSP satisfying minimum safety requirements can be used in practice. Such an approach taking into account no economic and environmental concerns cannot help sustain GCS growth in today's competitive freight transportation market. This paper introduces a revised evidential reasoning (ER) approach to cope with the complex decision-making problem associated with the CSP of GCS. The complexity mainly results from the dynamic interdependency between the decision criteria and alternatives. The revised ER can determine the functions of the safety-related criteria in the decision making process by considering the extent to which each decision alternative meets the minimum safety requirements. The model is tested in multiple forms by an empirical study using a national GSC loading laboratory, and a real-life application by a shipping company in practice. The results reveals that the new model can aid general ship owners to make sustainable CSPs from a multiple-dimensional perspective and select an optimal CSP based on specific voyage scenarios. [ABSTRACT FROM AUTHOR]