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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 framework is proposed using security-driven and safety-oriented methods. • SSCA is constructed through both top-down and bottom-up perspectives. • The regulatory development of SSCA is analysed among different transport modes. • It strikes new research directions for digitalised and autonomous transport vehicles. Transportation is sensitive to risk. Given the fast development of digitalisation and automation of transport systems in the past decade, new types of security risks (e.g. cyberattacks) emerge within the context of transport safety research. To enable the integrated analysis of emerging security and classical safety-related risks in a holistic manner, safety and security co-analysis (SSCA) is highly demanded for accident prevention. SSCA in transport systems will benefit the risk analysis of complex cyber physical transport systems facing challenges from both hazards and threats. However, the nature of hazard and threat-based risks is fundamentally different, which leads to the various difficulties of analysing them on the same plane. They include the use of different risk parameters, the uncertainty levels of the risk input and the methodologies of risk inference. To address such concerns, this study firstly reviews the literature on SSCA and compares the employed methodologies and their applications within the context of transport systems. Taking into account the advantages of both security-driven and safety-oriented methods, a conceptual framework is proposed to imply the insights on SSCA for transportation through both top-down and bottom-up perspectives, followed by a quantitative illustrative case study. Then, the regulatory development and evolution of SSCA in transport in practice is analysed across different transport modes, which configures initiatives' interrelations for a cross-fertilisation purpose. As a result, the findings reveal new research directions for the safety of digitalised and/or autonomous transport vehicles and aid in the formation of future transport safety study agendas. [ABSTRACT FROM AUTHOR]

As an administrative measure to ensure maritime safety, Port State Control inspections are implemented and regarded as an important line of defence in coping with potential maritime accidents. To reinforce its role, a New Inspection Regime (NIR) was developed and put into practice by Paris MoU in 2011. It is widely recognized that the implementation of NIR has transformed and modernized the PSC inspection system in the Paris MoU region, stimulated the vessel qualit, and further improved vessel quality. In this paper, the influence of the implementation of NIR on the PSC inspection system and vessel quality is revealed for the first time. Based on inspection data and records collected from the Paris MoU online database, a comparative analysis between the 'Pre-NIR' (time before the implementation of NIR) and 'Post-NIR' (time after the implementation of NIR) periods is conducted from two perspectives. A macroscopic approach is first adopted to characterise the overall changes in inspection results and inspected vessels' quality through a statistical analysis of extrinsic Key Performance Indicators (KPIs) such as detention rate and deficiency rate. Then, a microscopic analysis is conducted to assess the influence of the NIR on intrinsic attributes of the vessels and of the inspection results based on Bayesian Network models derived from Pre-NIR and Post-NIR periods. The findings of this research systematically reveal the aspects from which the NIR improves the PSC inspection system, vessel quality and maritime safety. It will generate significant impact on and contribution to the promotion and stimulation of the adoption of NIR in more ports and regions to improve safety at sea in the whole world. • Conduct a comparative analysis of the impact of New Inspection Regime on port state control inspection. • Characterise the changes in PSC inspections and inspected vessels' quality through a statistical analysis of extrinsic KPIs. • Assess the influence of the NIR on intrinsic attributes of the vessels and the inspections based on Bayesian Network models. • Reveal the aspects from which the NIR improves the PSC inspection system, vessel quality and maritime safety. [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]

Abstract This study proposes a measurement instrument for port performance in the context of container transport logistics (CTLs) by taking perspectives from different port stakeholders. An importance-performance analysis (IPA) is used to develop an analytical tool for investigating the importance and performance (IP) of major container ports in South Korea against individual CTLs criterion. The main originality of this study is the development of a measurement instrument to provide managerial and operational insights to both port managers (i.e. terminal operating companies) and policy makers (i.e. port authorities and government) for stakeholder management in CTLs. The analysis helps port managers and policy makers to converge the different objectives and concerns for better management. Highlights • Importance-performance analysis for port performance in the context of container transport logistics. • Taking different port stakeholders' perspective from port managers and policy makers. • Introduces both quantitative and qualitative port performance indicators. • Container ports from South Korea as examples. [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]

• A novel database of maritime piracy incidents capturing the characteristics of risk factors. • A holistic framework of spatio-temporal pattern mining and analysis of maritime accidents. • A novel time-series measurement method for time-based and spatio-temporal pattern analysis. • Advanced data-driven clustering and classification analysis of maritime piracy incidents. • Piracy spatial pattern analysis, findings, and implications against influential risk factors. Since the new century, we have witnessed the fast evolution of pirate attack modes in terms of locations, time, used weapons, and targeted ships. It reveals that the current understanding of pirate attack spatio-temporal patterns is fading, requiring new technologies of big data analysis to master the hidden rules of piracy-related risk spatio-temporal patterns and rationalize the development of relevant anti-piracy measures and policies. This paper aims to develop a new framework of spatio-temporal pattern mining to realize the visualization and analysis of maritime piracy incidents from different standpoints using a new piracy incident database generated from three datasets. Time-based, space-based, and spatial-temporal pattern mining of piracy incidents are systematically investigated to dissect the influence of different risk factors and mine the characteristics of the incidents. Moreover, a novel Fast Adaptive Dynamic Time Warping (FADTW) method is proposed to uncover the hidden temporal and spatial-temporal patterns of piracy incidents. Furthermore, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied to extract the spatial distribution patterns and discover the high-risk areas. Finally, risk factors-based classification exploration has uncovered different spatial patterns. The findings, showing the global and local features of piracy incidents, have made significant contributions to rationalizing anti-pirate measures for safe navigation. [ABSTRACT FROM AUTHOR]

Maritime Autonomous Surface Ships (MASS) are deemed as the future of maritime transport. Although showing attractiveness in terms of the solutions to emerging challenges such as carbon emission and insufficient labor caused by black swan events such as COVID-19, the applications of MASS have revealed problems in practice, among which MASS navigation safety presents a prioritized concern. To ensure safety, rational route planning for MASS is evident as the most critical step to avoiding any relevant collision accidents. This paper aims to develop a holistic framework for the unsupervised route planning of MASS using machine learning methods based on Automatic Identification System (AIS) data, including the coherent steps of new feature measurement, pattern extraction, and route planning algorithms. Historical AIS data from manned ships are trained to extract and generate movement patterns. The route planning for MASS is derived from the movement patterns according to a dynamic optimization method and a feature extraction algorithm. Numerical experiments are constructed on real AIS data to demonstrate the effectiveness of the proposed method in solving the route planning for different types of MASS. [ABSTRACT FROM AUTHOR]

Highlights • Analyse the impact of company performance in NIR on port state control policy. • Develop a risk prediction tool to reduce ships' detention likelihood of their ships. • Propose a dynamic risk-based game model for rationalise port state control policy. • Conduct an empirical study to provide useful insights for rational risk based port state control. Abstract This paper analyses the game relationship between port authorities and ship owners under the new inspection regime (NIR). Based on 49328 inspection reports from Paris Memorandum of Understanding (MoU) (2015-2017), we present a Bayesian Network (BN) model to determine vessel detention rates after adding company performance as a new indicator in PSC inspection. A strategic game model is formulated by incorporating the BN model outcomes. The optimal inspection rate from the game model can help improve port authority performance in PSC. An empirical study is conducted to illustrate the insights of the results and provide suggestions for port authorities. [ABSTRACT FROM AUTHOR]

In this paper, a modified car-following model is proposed by taking into account the influence of the average speed effect of vehicles and driver’s memory on traffic flow basing on two velocity difference model (TVDM). The stability conditions are obtained through the linear stability analysis. The time-dependent Ginzburg–Landau (TDGL) equation and the modified Korteweg–de Vries (mKdV) equation are derived in the unstable areas by means of nonlinear analysis, respectively. The TDGL and mKdV equations are constructed to describe the traffic behavior near the critical point. The evolution of traffic congestion and the corresponding energy consumption are discussed. The results from numerical simulations are consistent with the ones from theoretical analysis. It is found that the extended model can not only reduce energy consumption but also enhance the stability of traffic flow. [ABSTRACT FROM AUTHOR]

The Belt and Road (B&R) initiative was introduced by the Chinese government to promote the worldwide economic development and multilateral cooperation between China and the associated countries. As a crucial part of global supply chains, transportation plays a key role to ensure the implementation of the B&R. Safety is one of the issues with great importance in transportation research. However, its foci have been expanded from traditional risk through security to resilience and sustainability. Resilience has attracted considerable interests from both researchers and practitioners across different research domains in recent years. Various studies have been conducted on transportation resilience from different perspectives. Consequently, different definitions have been developed to define and describe resilience. This paper presents a systematic review on transportation resilience with emphasis on its definitions, characteristics, and research methods applied in different transportation systems/contexts. It aims to figure out what transportation resilience is and what kind of essential characters it usually has. More importantly, research challenges are analysed and a future research agenda on the resilience of transportation systems is proposed. This paper will provide comprehensive insights into understanding the transportation resilience, as well as establish new horizons for relevant research topics within the context of the B&R. [ABSTRACT FROM AUTHOR]

The long term impact posed by climate change risk remains unclear and is subject to diverse interpretations from different maritime stakeholders. The inter-dynamics between climate change and ports can also significantly diversify in different geographical regions. Consequently, risk and cost data used to support climate adaptation is of high uncertainty and in many occasions, real data is often unavailable and incomplete. This paper presents a risk and cost evaluation methodology that can be applied to the analysis of port climate change adaptation measures in situations where data uncertainty is high. Risk and cost criteria are used in a decision-making model for the selection of climate adaptation measures. Information produced using a fuzzy-Bayesian risk analysis approach is utilized to evaluate risk reduction outcomes from the use of adaptation measures in ports. An evidential reasoning approach is then employed to synthesize the risk reduction data as inputs to the decision-making model. The results can assist policymakers in developing efficient adaptation measures that take into account the reduction in the likelihood of risks, their possible consequences, their timeframe, and costs incurred. A technical study across 14 major container ports in Greater China is presented to demonstrate the interaction between cost and risk analysis, and to highlight the applicability of the stated methodology in practice. The paper offers a useful analytical tool for assessing climate change risks to ports and selecting the most cost-effective adaptation measures in uncertain conditions. It can also be used to compare the practitioners’ perceptions of climate risks across different geographical regions, and to evaluate improvements after implementation of the selected adaptation measures with potential budgetary constraints. The methodology, together with the illustrative cases, provides important insights on how to develop efficient climate change adaptation measures in a supply chain context to improve the sustainability of development and enhance adaptation measures for ports, port cities, intermodal transport, supply chains, and urban and regional planning in general. [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]

Port performance measurement (PPM) and comparison research, presenting a multiple criteria decision making (MCDM) issue in nature, has been intensively conducted by researchers from both decision science on modelling and port studies from empirical perspectives. Assigning an appropriate weight to each defined port performance indicator (PPI) is essential for rational decision and precise performance measurement. However, PPIs are often presented in a hierarchy, having the interdependency among them ignored. It causes concerns on the accuracy of PPIs’ weight allocation and arguments on the performance measurement results, revealing a significant research gap to be addressed. As far as MCDM modelling is concerned, the importance of criteria has been studied utilising either absolute or relative comparisons, while the calculation of their importance also takes into account both independency and interdependency factors. However, there is lack of empirical studies in the literature to provide supporting evidence to distinguish the different impacts of the two factors. This study aims to compare the analysis of PPIs importance when taking into account their independent relationship using an analytic hierarchy process (AHP) and their interdependent relationship using a decision making trial and evaluation laboratory (DEMATEL) incorporating an analytic network process (ANP), respectively. The same domain experts are invited to evaluate the importance of the defined PPIs based on both approaches. The results demonstrate that a similar variance of relative importance across the PPIs but a clear difference on their importance scores and ranking. As a result, the results make contributions to fulfil the research gap on consideration of interdependency among PPIs in PPM and on the provision of convincing empirical evidence to highlight the impact of interdependency of criteria on MCDM modelling. Another practical significance draw from this study is that use of DEMATEL can aid port stakeholders to make more rational decision as to whether the interdependency among PPIs should be taken into account in PPM and/or port choice. [ABSTRACT FROM AUTHOR]

This study develops a new port performance measurement model by taking the perspectives from different port stakeholders. The novelty lies in the modelling of interdependencies among port performance measures, and the combination of weights of interdependent measures with both qualitative and quantitative evaluations of the measures from multiple stakeholders for quantitative port performance measurement. It represents an effective performance measurement tool and offers a diagnostic instrument for performance evaluation and/or monitoring of ports and terminals so as to satisfy different requirements of various port stakeholders in a flexible manner. [ABSTRACT FROM AUTHOR]

• Propose a new conflict detection method to calibrate the conflict criticality of ships. • Apply a traffic complexity matrix to reveal the topological and evolutionary features. • Introduce an FCI-based approach to support traffic complexity pattern assessment. • Demonstrate the new model performance through scenario analysis and validation. Maritime traffic situational awareness plays a vital role in the development of intelligent transportation-support systems. The state-of-the-art study focuses on near-miss collision risk between/among ships but reveals challenges in estimating large-scale traffic situations associated with dynamic and uncertain ship motions at a regional level. This study develops a systematic methodology to evaluate ship traffic complexity to comprehend the traffic situation in complex waters. In the new methodology, the topological and evolutionary characteristics of ship traffic networks and the uncertainty in ship movements are considered simultaneously to realise probabilistic collision detection. The methodology, through the effective integration of probabilistic conflict estimation and traffic complexity modelling and assessment, enables the evaluation of traffic complexity in a fine-grained hierarchical manner. With the AIS-based trajectory data collected from the world's largest port (i.e. Ningbo-Zhoushan Port), a thorough validation of the evaluation performance is conducted and demonstrated through scenario analysis and model robustness. Moreover, some critical research results are obtained in terms of traffic network heterogeneity analysis; statistics including occurrence frequency, temporal distribution, life cycle, and transition probability of traffic complexity patterns; and correlation examination between the number of ships and traffic complexity patterns. These findings offer new insights into improving maritime traffic awareness capabilities and promoting maritime traffic safety management. [ABSTRACT FROM AUTHOR]

Maritime piracy and robbery can not only cause logistics chain disruption leading to economic consequences but also result in loss of lives, and short- and long-term health problems of seafarers and passengers. There is a justified need for further investigation in this area of paramount importance. This study analyses maritime piracy and robbery related incidents in terms of the major influencing factors such as ship characteristics and geographical locations. An analytical model incorporating Bayesian reasoning is proposed to estimate the likelihood of a ship being hijacked in the Western Indian or Eastern African region. The proposed model takes into account the characteristics of the ship, environment conditions and the maritime security measures in place in an integrated manner. Available data collected from the Global Integrated Shipping Information System (GISIS) together with expert judgement is used to develop and demonstrate the proposed model. This model can be used by maritime stakeholders to make cost-effective anti-piracy decisions in their operations under uncertainties. Discussions are given on industrial response to maritime piracy in order to minimize the risk to ships exposed to attacks from pirates. Further recommendations on how maritime security and piracy may be best addressed in terms of maritime security measures are outlined. [ABSTRACT FROM AUTHOR]

Global shipping flows through strategically important restricted waters such as Panama and Suez Canals, and hence accidents occurring in the Canals will cause serious disruptions to global supply chains. In this paper, a new data-driven Bayesian network (BN) based risk model is developed to investigate how risk factors jointly generate impact on different types of maritime accidents within restricted waters. A new risk database involving 25 factors has been developed by manual analysis of all the recorded accidents from 2005 to 2021 that occurred in the world's important restricted waters including key maritime canals, channels and straits. A data-driven BN model is constructed to analyse the key risk influential factors (RIFs) contributing to such accidents. The model is verified by sensitivity analysis and real accident cases. Further, it is tested by the already known information of the 2021 Suez Canal blockage case to generate useful insights and draw the lessons to learn. In a retrospective analysis using the currently available limited information on the Suez Canal case, the implication of the case study shows a plausible explanation for the observed findings by scenario analysis. The findings can be applied to backward risk cause diagnosis for accident investigation and forward risk prediction for accident prevention in restricted waters to avoid the reoccurrence of similar accident to the Suze Canal blockage. • A new data-driven risk model enables the systematic analysis of the risk factors of maritime accidents in restricted waters. • Analysis of the dependencies between RIFs for maritime accidents in restricted waters. • Forward risk prediction for accident analysis and prevention. • Backward risk diagnosis for root cause analysis of maritime accidents. • Analysis of the Suez Canal blockage case study to provide insights for accident investigation and prevention. [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]

Over the years, many efforts have been focused on developing methods to design seaport systems, yet disruption still occur because of various human, technical and random natural events. Much of the available data to design these systems are highly uncertain and difficult to obtain due to the number of events with vague and imprecise parameters that need to be modelled. A systematic approach that handles both quantitative and qualitative data, as well as means of updating existing information when new knowledge becomes available is required. Resilience, which is the ability of complex systems to recover quickly after severe disruptions, has been recognised as an important characteristic of maritime operations. This paper presents a modelling approach that employs Bayesian belief networks to model various influencing variables in a seaport system. The use of Bayesian belief networks allows the influencing variables to be represented in a hierarchical structure for collaborative design and modelling of the system. Fuzzy Analytical Hierarchy Process ( FAHP ) is utilised to evaluate the relative influence of each influencing variable. It is envisaged that the proposed methodology could provide safety analysts with a flexible tool to implement strategies that would contribute to the resilience of maritime systems. [ABSTRACT FROM AUTHOR]

Unsafe acts of ship officers have been a direct cause of many maritime accidents. In the maritime industry, much effort has been made to prevent unsafe acts and to improve safe behaviours of ship officers. The positive effect of organizational safety climate on human behaviour has been well documented in the literature. Within this context, this paper aims to pioneer the development of the measurement constructs of safety climate in shipping operations and analyze its impact on safety consciousness and behaviour of ship officers. Using primary data collected from 284 deck officers and captains, an exploratory and confirmatory factor analysis and a structural equation model are used to reveal the relationship among the constructs of safety climate, safety consciousness and safety behaviour. The research results show that (1) safety climate generates a direct positive effect on safety consciousness and behaviour; (2) safety consciousness has a positive effect on safety behaviour; and (3) safety consciousness acts as a mediator between safety climate and safety behaviour. Its managerial implications and practical contributions lie in the provision of useful insights on how ship managers can effectively gain the improved safety behaviours of ship officers by appropriate introduction of safety climate and consciousness. [ABSTRACT FROM AUTHOR]

This paper proposes a novel framework for analysing and synthesising engineering system risks on the basis of a generic Fuzzy Evidential Reasoning ( FER ) approach. The approach is developed to simplify the inference process and overcome the problems of traditional fuzzy rule based methods in risk analysis and decision making. The framework, together with the FER approach has been applied to model the safety of an offshore engineering system. The benchmarking between the new model and a well-established Rule based Inference Methodology using the Evidential Reasoning ( RIMER ) is conducted to demonstrate its reliability and unique characteristics. It will facilitate subjective risk assessment in different engineering systems where historical failure data is not available in their safety practice. [ABSTRACT FROM AUTHOR]

Highlights: [•] Develop a quantitative security assessment model using fuzzy evidential reasoning. [•] Identify the major generic key security performance indicators (KSPIs) in port. [•] Provide a transparent decision making tool for security control measures selection. [•] Rationalise resource allocation based on different risk levels of port facilities. [•] Use a real case study to demonstrate the developed methodology. [ABSTRACT FROM AUTHOR]

Abstract: The risks of hazards of liquefied natural gas (LNG) carrier operations and their fundamental causes are investigated in this study. A new framework is developed through the combination of a risk matrix approach and a fuzzy evidential reasoning (FER) method. The risk matrix approach is employed to identify the major hazards associated with the LNG carrier operations. In the risk matrix methodology, linguistic terms are used to estimate how likely hazards may occur in LNG carrier operations at sea, and the consequences of the hazards. The failures of an LNG spill from a transfer arm and an LNG containment system are identified as the hazards of “very high risk”. The fault tree analysis (FTA) diagrams of “very high risk” hazards (top events) are used to identify their causes (basic events) and failure logics. Due to the unavailability of historical failure data, the safety/risk level of each cause is investigated using the FER approach. In the FER approach, the safety levels of the basic events of the top event are assessed using three safety parameters (i.e. occurrence likelihood, consequence severity and failure consequence probability) and combined using evidential reasoning (ER) in fuzzy environment in order to reveal the top events’ safety/risk levels. [Copyright &y& Elsevier]

An introduction is presented in which the editor discusses various reports within the issue on topics including the regional transformation and port cluster competition, the intermodal transport, and port infrastructures.

• Elaborate all the indicators influencing airport climate resilience. • Develop a climate resilience evaluation method using evidential reasoning. • Use big data to quantify the airport climate resilience. • Conduct an empirical study to analyse the climate resilience of the UK airports. Due to increased extreme weather events, climate adaptation has become an essential issue to be addressed by all transport infrastructures, including airports. This paper aims to develop a Climate Resilience Indicator (CRI) framework for assessing airport climate resilience, which for the first time, considers: climate exposure, vulnerability and adaptive capacity simultaneously and advances the development of climate risk analysis of airports to a point where their adaptation and resilience can be quantified under uncertainty in data. Climate-related data was collected from multiple sources to evaluate an airport's performance against each indicator. An evidential reasoning (ER) approach is used to evaluate each airport by integrating all the indicators to derive its final CRI score. The findings provide valuable insights into how urgently an airport needs to deal with climate change and reveal information to help with resource allocation for different airports nationally through proactive adaptation planning. [ABSTRACT FROM AUTHOR]

Maritime autonomous surface ships (MASS), presenting the future of maritime transport, are attracting increasing attention from the international maritime community. The collision risk analysis of MASS reveals unsolved challenges, which without appropriate solutions, will result in the error prone development of the relevant risk control measures and policies. Among the challenges, two significant ones in the existing literature are the lack of historical failure data to realise quantitative risk assessment, and 2) the complex causal relationship among the relevant risk factors. This paper aims to develop a new Fault Tree Analysis-Fuzzy Bayesian Network (FTA-FBN) model to conduct the collision risk assessment of MASS with uncertainty in data. First, it establishes a causal relationship among the risk factors through an FTA. Secondly, mapping the obtained FTA diagram into a BN allows fault diagnosis and the identification of the most important factors influencing MASS collisions. In this process, a survey is conducted to collect the primary data for configuring the conditional probabilities of the relevant influential factors and quantifying the developed BN for risk diagnosis and prediction. Finally, the new model is verified by using sensitivity analysis and three axioms and then applied to conduct scenario-based risk prediction and diagnosis to generate insightful findings to guide MASS navigation safety. The results demonstrate that the FTA-FBN model realizes the simplification of the expert scoring process, reduces computational complexity, and addresses the challenge of constructing causal relationships between MASS collisions and their risk factors due to the scarcity of historical accident data. Additionally, the BN backward reasoning identifies key collision risks, including external physical attacks, inadequate training of shore-based operators, insufficient maintenance of ship equipment and systems, and cyber-security threats. The new model when being adapted, can provide a reference for the formulation of safe navigation policies and provide important insights for shipping companies to ensure the safe navigation of their ships and shipbuilders to optimise ship design. • A new FTA-FBN model for MASS collision risk assessment in the presence of data uncertainty. • Primary data from dozens of domain expert opinions for MASS collision risk analysis. • Forward risk prediction and backward risk diagnosis of MASS collision risk. • Identification of the most important risk factors influencing MASS collisions. [ABSTRACT FROM AUTHOR]

Ship-at-berth emissions significantly affect air quality and health of human beings in a port and its neighbourhood. However, it is challenging to estimate these emissions precisely due to stringent data requirements. Shore Power (SP) data, including its actual energy consumption and duration, offers useful insights to refine these estimates, but has yet to be fully explored. This study proposes a novel scheme incorporating SP data to improve the accuracy of containership-at-berth emission estimates and evaluate emission reduction measures. The findings reveal substantial differences among existing emission estimates from identical case studies, highlighting the importance of SP data. Additionally, it demonstrates significant emissions from low-load main engines and confirms the efficacy of SP in emission reduction. These findings provide valuable insights into emission estimation methods and their potential applications in estimating emission reduction measures, underlining the importance of policy support in facilitating the SP implementation. [ABSTRACT FROM AUTHOR]

• Based on the encounter evolution, a method is proposed to enable the construction of a ship collision risk model. • A PVO model is developed with an elliptic conflict region, utilizing d dv and t dv to quantify state risk of velocities. • A collision risk evolution model is introduced, incorporating spatial attributes, macro-level and micro-level decisions. • Historical scenarios and encounter stages are employed to determine model parameters and their weights. Aiming at realizing collision risk quantitative evaluation among encounter ships, a novel data-driven evolution model is proposed concerning encounter evolution in maritime transportation. A probabilistic velocity obstacle with an elliptic conflict region is constructed by taking into account uncertainty. The degree of and time to domain violation are introduced to quantify collision risk levels under varying velocities. Then, a ship collision risk evolution model is formulated by considering spatial attributes, macro-level and micro-level evolution based on a realistic collision avoidance decision. The model parameters and their weights are determined by statistical analysis of historical encounter scenarios and the characteristics of encounter stages. Therefore, the model encapsulates the statistical characteristics of actual data, which improves its practical values. The results of case studies indicate that the collision risk evolution model can properly reflect collision risk, so that collision evolution stages can be classified accordingly for rational anti-collision guidance. It brings new contributions to risk visualization, collision avoidance decision-making, and collision accident analysis and responsibility determination. [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]

This paper investigated the opportunities and challenges of integrating ports into hydrogen (H2) supply chains in the context of Australia and Japan because they are leading countries in the field and are potential leaders in the upcoming large-scale H2 trade. Qualitative interviews were conducted in the two countries to identify opportunities for H2 ports, necessary infrastructure and facilities, key factors for operations, and challenges associated with the ports' development, followed by an online survey investigating the readiness levels of H2 export and import ports. The findings reveal that there are significant opportunities for both countries' H2 ports and their respective regions, which encompass business transition processes and decarbonisation. However, the ports face challenges in areas including infrastructure, training, standards, and social licence, and the sufficiency and readiness levels of port infrastructure and other critical factors are low. Recommendations were proposed to address the challenges and barriers encountered by H2 ports. To optimise logistics operations within H2 ports and facilitate effective integration of H2 applications, this paper developed a user-oriented working process framework to provide guidance to ports seeking to engage in the H2 economy. Its findings and recommendations contribute to filling the existing knowledge gap pertaining to H2 ports. [ABSTRACT FROM AUTHOR]

• Major hazards influencing cycling safety are identified from various resources. • A novel BN model is developed to enable the prediction of cycling accident severity. • The influence of major hazards on cycling accident severity are evaluated. • New findings of useful insights for improving cycling safety are derived. • Safety suggestions are provided to reduce accident severity and ensure cycling safety. Cycling helps reduce traffic congestion, environmental pollution and promote a healthy lifestyle for the general public. However, it could also expose cyclists to dangerous environments, resulting in severe consequences and even death. Transport authorities are seeing growing accidents in city regions with increasing cycling population, requiring the development of new risk informed cycling safety policies. This paper aims to develop a new conceptual risk analysis approach based on a Bayesian network (BN) technique to enable the analysis and prediction of the severity of cycling accidents. To identify the risk factors influencing accident severity, 2,269 cycling accident reports from the UK city region were manually collected, where primary data was extracted and analysed. An advanced data training method (i.e. Tree Augmented Naïve Bayes (TAN)) for BN development was applied to investigate their correlation and their individual and combined contributions to cycling accident severity. As a result, the risk factors influencing accident severity are prioritised in terms of their risk contribution. The risk levels of accident severity can be predicted in dynamic situations based on the data from simulated and/or real cycling environments. The findings can provide useful insights for making rational cycling safety policies in proportion to different risk levels. [ABSTRACT FROM AUTHOR]

• Identify Key Green Performance Indicators (KGPIs) affecting consumer choice on green vehicles. • Use advanced fuzzy evidential reasoning model to quantify the cleanliness of vehicles. • Develop a scoring mechanism to assess the buyers' behaviours on purchasing green vehicles. • Provide useful information to guide buyers towards the cleanest available vehicles. This paper aims to develop a testing and scoring mechanism, to assess the buyers' behaviours on purchasing green vehicles. As there are various vehicle types with different emission properties presented in the current market, a Key Green Performance Indicator (KGPI) framework is designed to choose vehicles by comparing all the relevant factors affecting consumer choice on green vehicles, including different emission damages, buyer cost, car performance, and other incentives. The novelty of this paper is to combine all the factors to produce a unique single score to present vehicle cleanliness and guide buyer choices. The findings will provide useful information to guide buyers towards the cleanest available vehicles and promote the manufacturing of new cleaner vehicles to the market. [ABSTRACT FROM AUTHOR]

Modelling the interdependencies among the factors influencing human error (e.g. the common performance conditions (CPCs) in Cognitive Reliability Error Analysis Method (CREAM)) stimulates the use of Bayesian Networks (BNs) in Human Reliability Analysis (HRA). However, subjective probability elicitation for a BN is often a daunting and complex task. To create conditional probability values for each given variable in a BN requires a high degree of knowledge and engineering effort, often from a group of domain experts. This paper presents a novel hybrid approach for incorporating the evidential reasoning (ER) approach with BNs to facilitate HRA under incomplete data. The kernel of this approach is to develop the best and the worst possible conditional subjective probabilities of the nodes representing the factors influencing HRA when using BNs in human error probability (HEP). The proposed hybrid approach is demonstrated by using CREAM to estimate HEP in the maritime area. The findings from the hybrid ER-BN model can effectively facilitate HEP analysis in specific and decision-making under uncertainty in general. • Identify the shortcomings of traditional human reliability analysis methods in maritime applications. • Develop a quantitative human reliability analysis method using fuzzy Bayesian evidential reasoning. • Rationalise subjective probability distribution in BNs to realise marine engineers' failures under uncertainty. • Demonstrate advantages of the new risk model through a real case analysis. [ABSTRACT FROM AUTHOR]

• Assess national Food Supply Chain (FSC) resilience by combining production-to-supply ratios and shipping connectivity. • Analyse the UK's FSC resilience, concerning the impacts from global challenges. • Comarative analysis of four countries to appreciate diverse FSC dynamics. • Uncover strengths and vulnerabilities of FSCs of developed and developing countries. • Offered strategic insights for policymakers in managing food security and disruptions. With the development of globalisation, countries such as the United Kingdom (UK) heavily rely on shipping for food imports and supplies. With the unpredictable impacts of black swan events, such as the Coronavirus disease (COVID-19) outbreak and the armed conflict between Ukraine and Russia, which are traditional food export countries, global freight supply chains could dramatically change, which significantly affects the global food supply chain (FSC) resilience, requiring new solutions to be found. Therefore, it is vital to assess a country's food importing system by sea to ensure its FSC resilience. However, it is challenging to analyse different food supply resilience levels on a national scale given the high uncertainties in the key relevant elements (e.g. food supplies and transportation) influencing FSC resilience. This paper aims to develop a new method to assess FSC resilience at a country level by pioneering the combination of the two most relevant attributes, its production-to-supply ratio and shipping transport connectivity, used to present food supply security separately in the current literature. Within this context, food production and import distance affecting the connectivity of various food supplies are selected to estimate FSC resilience in this study. The findings, including a new index framework to assess national food resilience, significantly contribute to a country's food security and the rational development of countermeasures and policies when necessary. To demonstrate the significance of the findings, the resilience of the UK FSC is first evaluated in a real case study, followed by a comparative study with Canada, Australia, Democratic Republic of the Congo (DRC) to identify the strengths and weaknesses of the FSCs of different countries. The findings of the studies can be used to monitor FSC resilience of the countries and provide rational policies for enhancing FSC resilience. [ABSTRACT FROM AUTHOR]

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]

The safety of maritime transportation along the twenty-first century Maritime Silk Road (MSR) is important to ensure its development and sustainability. Maritime transportation poses risks of accidents that can cause the death or injury of crew members and damage to ships and the environment. This paper proposes a Bayesian network (BN) based risk analysis approach that is newly applied in the main route of the MSR to analyse its relevant maritime accidents. The risk data are manually collected from the reports of the accident that occurred along the MSR. Next, the risk factors are identified and the results from the modelling method can provide useful insights for accident prevention. Historical data collected from accident reports are used to estimate the prior probabilities of the identified risk factors influencing the occurrence of maritime accidents. The results show that the main influencing factors are the type and location of an accident and the type, speed, and age of the involved ship(s). In addition, scenario analysis is conducted to analyse the risks of different ships in various navigational environments. The findings can be used to analyse the probability of each possible maritime accident along MSR and to provide useful insights for shipowners' accident prevention. [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]

• Analyse the impact of climate change on road transport systems. • Develop a risk prediction tool to reduce climate impact on roads. • Conduct economic analysis of various measures to reduce climate impact on roads. • Conduct an empirical study to provide useful insights for rational risk based climate adaptation. This paper aims to analyse the impacts of climate change to the current and predicted future situations of road transportation in the UK and evaluate the corresponding adaptation plans to cope with them. A conceptual framework of long-term adaptation planning for climate change in road systems is proposed to ensure the resilience and sustainability of road transport systems under various climate risks such as flooding and increased temperature. To do so, an advanced Fuzzy Bayesian Reasoning (FBR) model is first employed to evaluate the climate risks in the UK road transport networks. This modelling approach can tackle the high uncertainty in risk data and thus facilitate the development of the climate adaptation framework and its application in the UK road sector. To examine the feasibility of this model, a nationwide survey is conducted among the stakeholders to analyse the climate risks, in terms of the timeframe of climate threats, the likelihood of occurrence, the severity of consequences, and infrastructure resilience. From the modelling perspective, this work brings novelty by expanding the risk attribute "the severity of consequence" into three sub-attributes including economic loss, damage to the environment, and injuries and/or loss of life. It advances the-state-of-the-art technique in the current relevant literature from a single to multiple tier climate risk modelling structure. Secondly, an Evidential Reasoning (ER) approach is used to prioritise the best adaptation measure(s) by considering both the risk analysis results from the FBR and the implementation costs simultaneously. The main new contributions of this part lie in the rich raw data collected from the real world to provide useful practical insights for achieving road resilience when facing increasing climate risk challenges. During this process, a qualitative analysis of several national reports regarding the impacts posed by climate change, risk assessment and adaptation measures in the UK road sector is conducted for the relevant decision data (i.e. risk and cost). It is also supplemented by an in-depth interview with a senior planner from Highways England. The findings provide road planners and decision makers with useful insights on identification and prioritisation of climate threats as well as selection of cost-effective climate adaptation measures to rationalise adaptation planning. [ABSTRACT FROM AUTHOR]

This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces). [ABSTRACT FROM AUTHOR]

• A data-driven machine learning based BN model is developed to improve the inspection efficiency in PSC inspections. • New risk variables influencing the duration of ship detention are identified and the major deficiency types in different port states are revealed. • The influence of different deficiency types on the duration of detention is analyzed to generate managerial insights for safety management of port authorities. Port State Control (PSC) inspections are essential for port authorities to improve vessel quality and ensure maritime safety worldwide. However, the increasing frequency and duration of ship detentions indicate serious deficiencies of visiting vessels still largely exist, highlighting the urgent need for scientific solutions. This research aims to improve the efficiency of inspection policy and reduce the duration of detention by developing a data-driven Bayesian Network (BN) model using an improved machine-learning (ML) based methodology. New risk variables influencing the duration of ship detention, especially deficiency types, are identified based on the established database containing detention records within the jurisdiction of the Paris MoU from January 2015 to March 2022. Thorough analysis using the developed model allows the identification of deficiency types with a significant impact on the duration of detention, the discovery of interdependencies between these types and the clarification of the major and abnormal deficiency types in different port states. Policy implications and managerial recommendations for port authorities are presented. These include developing clear instructions on types of deficiencies that significantly impact detention time and proposing a selection strategy for vessels in different countries based on their specific circumstances. The proposed model utilizes big data analytics to support the development of inspection policies that are rational and effective. This research will provide good reference for effectively reducing the duration of ship detention, providing policy recommendations, improving ship standards, and ensuring maritime safety. [ABSTRACT FROM AUTHOR]

Ship trajectory prediction based on Automatic Identification System (AIS) data has attracted increasing interest as it helps prevent collision accidents and eliminate potential navigational conflicts. Therefore, it is necessary and urgent to conduct a systematic analysis of all the prediction methods to help reveal their advantages to ensure safety at sea in different scenarios. It is particularly important and significant within the context of unmanned ships forming a new hybrid maritime traffic together with manned ships in the future. This paper aims to conduct a comparative analysis of the up-to-date ship trajectory prediction algorithms based on machine learning and deep learning methods. To do so, five classical machine learning methods (i.e., Kalman Filter, Gaussian Process Regression, Support Vector Regression, Random Forest, and Back Propagation Network) and eight deep learning methods (i.e., Recurrent Neural Networks, Long Short-Term Memory, Bi-directional Long Short-Term Memory, Gate Recurrent Unit, Bi-directional Gate Recurrent Unit, Sequence to Sequence, Spatio-Temporal Graph Convolutional Network, and Transformer) are thoroughly analysed and compared from the algorithm essence and applications to excavate their features and adaptability for manned and unmanned ships. The findings reveal the characteristics of various prediction methods and provide valuable implications for different stakeholders to guide the best-fit choice of a particular method as the solution under a specific circumstance. It also makes contributions to the extraction of the research difficulties of ship trajectory prediction and the corresponding solutions that are put forward to guide the development of future research. • Multi-perspective systematic analysis of ship trajectory prediction (STP) research. • Comparative analysis of thirteen methods to reveal their advantages and disadvantages. • Applicability of the thirteen extracted methods under difficult STP circumstances. • New solutions to STP for future hybrid traffic of manned and unmanned ships. [ABSTRACT FROM AUTHOR]

In view of the huge contribution of transportation to global greenhouse gas emissions, it is imperative to embrace more carbon-efficient transportation modes to support our environmental goals. However, few studies offer empirical evidence to evaluate the potential of shifting transportation model for carbon emissions reduction. This paper, aiming at addressing this gap, conducts an empirical study to assess the CO2 emissions reduction through modal shift from road to waterway transport (MSRW). It utilizes primary data collected from more than 200 voyages of 92 enterprises through one national pilot project on CO2 emission reduction in the Quzhou region initiated by the Chinese central government. Specifically, it employs empirical analysis based on bottom-up methodologies to investigate the potential for CO2 emission reduction through MSRW. The results reveal that MSRW can aid to benefit 45,907 tons CO2 emission reduction from the modal shift within the study scope. When considering factors such as distance and voyage density, it provides new quantitative insights into the advantages of water transport over road transport in terms of CO2 emission reduction under different scenarios. Consequently, this study makes new contributions to the quantification of the benefits that an investigated region/city can derive from transport modal shift. It thereby lays the groundwork for effective cost-benefit analysis and policy implementation toward cleaner transportation. • Use of a hybris of energy consumption approach from a new analysis perspective that consider multiple-dimensional MSRW. • Provide a new research method to enable the quantification of the carbon emissions of an entire transport chain in detail. • Provides a solution to the evaluation of the effect of the implementation of transport policies on carbon emissions. • Investigate the MSRW to stimulate cost-benefit analysis and transport policy implementation towards clean transportation. [ABSTRACT FROM AUTHOR]

In order to analyse the research evolution and knowledge frontier in the research of marine accidents, 491 literatures on marine accidents in the Web of Science database from 2000 to 2022 are taken as data sources. Integrated with literature analysis of traditional method, CiteSpace and VOSviewer are then jointly used for the development of the knowledge network map and cluster analysis, and the knowledge of network map, research hotpots, research evolution and knowledge frontiers is obtained. It is found that there is a close cooperative relationship among journals, researchers, research institutions and countries or regions. According to the subjects and methods, the study of marine accidents can be divided into two parts: the analysis of the influential factors and accident consequences, as well as the methodology development of traditional and emerging technology. In this context, the analysis of human factors in remote-controlled ships, the prevention of accidents in Arctic waters have become research hotspots, while emerging accident analysis methods such as machine learning and big data mining also have shown powerful insights in the analysis of marine accidents. In terms of innovation, the bibliometric approach enhances the ability to handle large literature databases and conduct network analysis. Moreover, this study visualises collaborative networks, analyses evolution trends, reveals the research hotpots, and conducts a comparison and discussion of mainstream approaches in marine accident research. As a result, this study provides a theoretical basis and implementation direction for the development of maritime safety. • A systematic review and outlook of marine accident research is conducted. • A more comprehensive literature database of marine accident research is established. • The research hotpots and knowledge frontiers of marine accident research are identified. • The contributions, innovations and the future directions of marine accident research are discussed. [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]

The existing literature on the hydrogen supply chains has knowledge gaps. Most studies focus on hydrogen production, storage, transport, and utilisation but neglect ports which are nexuses in the supply chains. To fill the gap, this paper focuses on ports' readiness for the upcoming hydrogen international trade. Potential hydrogen exporting and importing ports are screened. Ports' readiness for hydrogen export and import are reviewed from perspectives of infrastructure, risk management, public acceptance, regulations and standards, and education and training. The main findings are: (1) liquid hydrogen, ammonia, methanol, and LOHCs are suitable forms for hydrogen international trade; (2) twenty ports are identified that could be first movers; among them, twelve are exporting ports, and eight are importing ports; (3) ports' readiness for hydrogen international trade is still in its infancy, and the infrastructure construction or renovation, risk management measures, establishment of regulations and standards, education and training all require further efforts. • Introducing ports' features in international hydrogen supply chains. • Identifying possible early hydrogen exporting and importing ports. • Identifying research gaps of ports' readiness for exporting and importing hydrogen. [ABSTRACT FROM AUTHOR]