Context Of Shipping Research Articles

Human errors analysis for remotely controlled ships during collision avoidance

To address human errors in collision avoidance tasks of remotely controlled ships, this study aims to develop a comprehensive framework for human error analysis within the context of autonomous ships. Firstly, the Hierarchical Task Analysis method is utilized to identify crew collision avoidance tasks associated with the traditional ship, and these tasks are then dissected into different operational stages using the Information Decision Action in a Crew cognitive model. Secondly, a combination of the fault hypothesis method and expert opinions are used to identify potential human error that may occur during collision avoidance operations of remotely controlled ships. Thirdly, an integrated approach is proposed to build a quantitative risk assessment model, which combines Failure Mode and Effects Analysis, Evidential Reasoning, and Belief rules-based Bayesian Network. Then, axiomatic analysis is used to verify the robustness and applicability of the risk assessment model. Finally, based on the results of quantitative risk assessment, specific measures are proposed for enhancing the safety of collision avoidance process of remotely controlled ships. The findings show that uncoordinated interactions of human-computer systems during the decision-making stage are a pivotal factor in the collision avoidance process. Therefore, future design efforts for remote-control centre should prioritize improving the clarity of human-computer interaction interfaces.

Assessing the resilience of sustainable autonomous shipping: New methodology, challenges, opportunities

This paper introduces a resilience assessment methodology for sustainable autonomous maritime transport networks developed by the European project entitled “Advanced, Efficient, and Green Intermodal Systems” (AEGIS). This problem being addressed in this paper concerns the investigation of threats, incidents, and risks in an autonomous- and sustainable shipping context, and the research question is the development of both preventive measures and reactive actions to maintain an acceptable level of operational constraints. The paper’s methodology aids in designing sustainable logistics systems for highly automated waterborne transport, identifying threats and barriers to mitigate event consequences, thereby facilitating a seamless green transition. To examine the usability, this methodology is applied in a case study for cargo transportation, where we in this paper consider the maritime corridor between Trondheim and Rotterdam. The findings encompass the spectrum of possible actions to prevent and mitigate unwanted events and enhance resilience and flexibility. This can be used as a tool to respond to unwanted threats, enhance safety, and introduce new strategies. These results are deemed important as resilience is one of the prerequisites for the development of a sustainable transport system. This is true both for the companies that are engaged in the operation of such systems and for policymakers.

Ship trajectory prediction based on machine learning and deep learning: A systematic review and methods analysis

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.

Incorporation of seafarer psychological factors into maritime safety assessment

Psychological factors have been a critical cause of human errors in sectors such as health and aviation. However, there is little relevant research in the maritime industry, even though human errors significantly contribute to shipping accidents. It becomes even more worrisome given that seafarers are changing their roles onboard ships due to the growth of automation techniques in the sector. This research pioneers a conceptual framework for assessing seafarer psychological factors using neurophysiological analysis. It quantitatively enables the psychological factor assessment and hence can be used to test, verify, and train seafarers' behaviours for ship safety at sea and along coasts. A case study on ship collision avoidance in coastal waters demonstrates its feasibility using ship bridge simulation. An experimental framework incorporating neurophysiological data can be utilised to effectively evaluate the contribution of psychological factors to human behaviours and operational risks. Hence, it opens a new paradigm for human reliability analysis in a maritime setting. This framework provides insights for reforming and evaluating operators’ behaviours on traditionally crewed ships and in remote-controlled centres within the context of autonomous ships. As a result, it will significantly improve maritime safety and prevention of catastrophic accidents that endanger oceans and coasts.

Is entropy an indicator of port traffic predictability? The evidence from Chinese ports

Historical time-series data of container traffic in ports are characterised by strong randomness that causes the time-series prediction performance to vary significantly. In this sense, the prediction accuracy differs as a function of exogenous variables related to port characteristics and the maritime shipping context. Thus, the measuring problem of a priori container traffic predictability (i.e., the ability of methods to predict port traffic variables) arises. Academia suggests that this predictability may be related to entropy as a measure of the information provided. This paper analyses the Sample Entropy (SampEn), as an indicator of the complexity represented by the container traffic time-series data, comparing it with the error metrics provided by a statistical prediction and an artificial intelligent method. The Autoregressive Integrated Moving Average (ARIMA) and Back Propagation (BP) Neural Network are used to obtain the prediction performance of container port traffic in a data set composed of 20 Chinese ports and 5 selected international ports. Unexpectedly, the results indicate that the correlation between the SampEn and the prediction performance of the time series is weak. The complex ecosystem of the container shipping sector and the different port characteristics are suggested as the cause of this poor correspondence. Our findings help to understand the relationship between entropy and the predictability of time-series data in a sector (i.e. port traffic), which has not been explored before.

Reliability of Lower-Cost Sensors in the Analysis of Indoor Air Quality on Board Ships

Air quality in and around ships is governed by a variety of pollution sources that are unique for the shipping context. This makes the living and working conditions on ships substantially different from situations in cities or inside buildings. To gain insight into these differences, information about trends and absolute pollutant amounts on board ships is needed. However, the installation of reference instruments to monitor NO2, NO, O3, particulate matter and other environmental parameters is often not possible because of their size, weight or because of safety reasons. For that reason, more compact devices incorporating a variety of sensors are a good alternative. However, the use of such sensors is only possible when their behaviour and performance in a shipping context are well understood. To study this context, we were allowed to compare sensor-based measurements performed on a 36-year old ship dedicated to near shore operations with measurements of reference-grade instruments. Additional behavioural information of sensors is obtained by measuring campaigns organized on several inland ships. This contribution demonstrates that trends registered by gas and particulate matter sensors are reliable but that insufficient detection limits, higher noise, imperfect calibration and sensor errors result in some reliability constraints.

Cyber security risk assessment in autonomous shipping

Autonomous ships would require higher cyber-physical interaction in comparison with traditional shipping operations, thus increasing the vulnerabilities associated with cyber security. The increasing complexity surrounding the innate characteristics of the shipping industry makes it challenging to build a resilient framework for ensuring cyber security. This study proposes a multi-criteria decision-making (MCDM) framework for assessing cyber security risk in the autonomous shipping context. The research was validated through surveying subject matter experts, system designers and seafarers. Different types of equipment and systems are ranked based on their perceived vulnerability to cyber threats. Survey data from 28 subject matter experts are collected and analysed through the Bayesian best–worst method (BWM). At system level, the results indicate that navigational systems are the most vulnerable to potential cyber threats, while propulsion systems are the least vulnerable element in the context of future autonomous shipping operations. On a sub-system level, the three most vulnerable parts are Global Navigation Satellite System (GNSS), Electronic Chart Display and Information System (ECDIS) and the communication devices on shore control centres (SCC), while the least vulnerable parts are engine controls, SCC integration platforms and cargo handling at ports.

Industrial revolutions and transition of the maritime industry: The case of Seafarer’s role in autonomous shipping

Digital transformation and automation in the shipping industry is resulting disruptive changes to ship design, operations, and manning that aim to enhance safety, efficiency, and the environmental sustainability of maritime logistics. While there is growing research interest in these areas, examining the role of human element in the new smart shipping context is largely neglected. Through a systematic literature review, this paper aims to explore the multi-dimensional impact of autonomous shipping technology resulting from the application of Industry 4.0 and future industrial revolutions on seafarers. The impacts include the changing role of seafarers on-board and the strategies required to engage seafarers in their transition from traditional shipping to autonomous and smart shipping. The paper concludes that Industry 4.0 is being challenged for its shortfall in recognition of the importance of human role and its intelligence in the expected current industrial revolution. As a result, there is a demand to look further and beyond Industry 4.0 by introducing the next generation of industrial revolution, namely Industry 5.0. This paper suggests that the impact of this revolution in the maritime industry can be defined by concepts such as Maritime 5.0, Shipping 5.0, Seafarer 5.0, Maritime Education and Training 5.0 (MET 5.0).

Shipping optimization systems (SOS) for tramp: stochastic cargo soft time windows

Cargo time windows have been discussed in the shipping literature in several ways and within several contexts. One way considers the hard time windows, where a fixed date is assigned to both the open and close laycan for the cargo loading and discharging ports. According to this way, the cargo is rejected if the ship cannot meet these time windows. Another way develops soft time windows, where the ship for an additional cost or a penalty may violate the cargo laycan. The context in the previous ways includes a tramp deterministic model. In this paper, the soft time windows are considered within a context of a tramp stochastic model, where both the cargo transport demand and the laycan dates are random variables. The objective is a gross-profit-per-day accompanied with realistic shipping elements embedded into decision support systems known as shipping optimization systems (SOS). The chance-constraint programming and the modified version of the Dantzig–Wolfe decomposition principle known as block-angular linear ratio programming solve the problem. A case study demonstrates that using stochastic soft time windows with stochastic cargo transport demand has considerably improved the gross profit-per-day better than trying to arrive on time by varying ship speed or being within a non-stochastic shipping context.

Application of fuzzy bow-tie risk analysis to maritime transportation: The case of ship collision during the STS operation

Risk analysis is of paramount importance in maritime transportation due to the particularly hazardous nature of the work. While the International Maritime Organization (IMO) has circulated Formal Safety Assessment (FSA) for the assessment of on-board risk, it has not introduced a specific tool for risk analysis, and a more proactive approach to the assessment of risk is consequently sought by maritime safety researchers. This paper aims to demonstrate how the fuzzy bow-tie method of analysis might be deployed to quantitatively analyse risk. Focussing on the specific context of ship to ship (STS) cargo operations enables specific insights into the method to be gained. The findings of the research can be utilised by maritime safety professionals (STS superintends, mooring masters, tanker safety inspectors, company fleet managers, and tanker shipowners) in order to mitigate potential risks and enhance marine safety during the STS operation.

A risk assessment model with systematical uncertainty treatment for container shipping operations

ABSTRACT The substantial adverse effects of risk factors on container shipping and logistics promoted a deep integration of risk analysis into the decision-making process. This paper aims to develop a well-grounded quantitative model to operational risk in a container shipping context. Considering uncertainty as a primary component of the risk concept, methods were employed in an inter-complementary manner to enable not only a sense of foreseeability but also a deeper look into the weaknesses of the knowledge base. The intersubjectivity of the input extraction process was supported by the Evidential Reasoning (ER) algorithm. Risks are then assessed based on a Fuzzy Rules Bayesian Network (FRBN) model with a 2-level parameter structure before meaningful interpretations can be derived through a new risk mapping approach. Besides an illustrative case study, the model was tested by sensitivity analysis and an examination of multiple validity claims.

An evaluation of alliance motives, shipping alliances, and alliance performance in Taiwan

We introduce the transaction cost and market entry views as two critical motivations for shipping alliances and argue that shipping alliances increase alliance performance (i.e., improve oversupply, financial distress, and excessive competition). This study empirically evaluated the effects of transaction cost, market entry, and shipping alliances on alliance performance in a maritime shipping context. Data were collected from a survey of the Directory of the National Association of Shipping Agencies and Companies. A factor analysis was employed to identify the key dimensions, and a subsequent analysis was conducted using a structural equation model to test the research hypotheses. The findings show that shipping alliances act as a mediator variable between transaction cost, market entry, and alliance performance. The theoretical contributions and managerial implications are proposed to serve as a basis for a wider discussion of shipping alliances.

Performance analysis of crowd-shipping in urban and suburban areas

Crowd logistics is a novel shipping concept where delivery operations are carried out by using existing resources, namely vehicle capacity and drivers from the crowd, thereby offering potential for economic, social, and environmental benefits. Despite the promise of this new logistics model, little is known about its actual functioning, performance, and impact. This paper presents a pioneering study of the performance of a real crowd-shipping system in the U.S. using empirical data from 2 years of operations. We contribute to the literature by: (1) defining performance metrics and developing models that account for the specificity of crowd-shipping systems by distinguishing the essential stages from bidding to acceptance and delivery of shipments, (2) identifying the significant covariates, including shipment features, built environment, and socio-demographic factors giving rise to different delivery performance outcomes, and (3) deriving sensitivity analysis to study the performance and implications of crowd-shipping in urban and suburban areas. The analysis is formalized as two-level nested logit models with nests representing bidding and delivery outcomes. The results show that not only does the delivery outcome performance vary significantly between urban and suburban areas, but the explanatory factors also vary significantly for the two contexts. Additionally, several factors have ambiguous impacts depending on the stage. Larger shipment size (versus strict deadlines) leads to increasing (decreasing) the likelihood of bids being placed, while having the opposite effect when it comes to the delivery phase. The findings highlight the need for developing different strategies to foster and improve the performance of this novel system depending on both the urban–suburban shipping context and the stage of delivery.

Organizational motivation, employee job satisfaction and organizational performance

Purpose The purpose of this study is to evaluate the effect of motivation on job satisfaction and organizational performance in the context of container shipping companies in Taiwan. Four motivation dimensions were identified based on an exploratory factor analysis, including remuneration, job achievement, job security and job environment. In addition, five job satisfaction dimensions were identified, namely: job policy, job autonomy, job workload, job performance and job status. Organizational performance dimensions included financial and non-financial performance. Design/methodology/approach Factor analysis was used to summarize a large number of motivation, job satisfaction and organizational performance attributes to identify the crucial factors. Reliability tests based on Cronbach’s alpha and corrected item-total correlation coefficients was used to test the internal consistency of questionnaire responses. ANOVA tests were subsequently used to test for differences in respondents’ perception of these factors according to selected demographics. Finally, a multiple regression model analysis was conducted to examine the relationships between motivation, job satisfaction and organizational performance. Findings Results indicated that remuneration and job performance had a positive effect on financial performance dimensions such as return on assets, turnover growth rate and profitability while job environment and job autonomy had a positive effect on non-financial performance dimensions, such as customer service, employee productivity and service quality. Originality/value This study has drawn attention to the importance of the relationships between motivation, job satisfaction and organizational performance in the container shipping context. The findings have significant implications for researchers and shipping practitioners. Despite the existence of research on the inter-relationships between motivation, job satisfaction and organizational performance in other disciplines, no empirical study was discussed in previous shipping or transportation-related research.

The effect of CSR practices on organisational performance in the container shipping in Taiwan

This study empirically examined the relationships between corporate social responsibility (CSR), customer satisfaction, customer loyalty, and organisational performance in the container shipping context. We collected data from a survey of 124 container shipping firms and agencies and used a structural equation model to test the research hypotheses. We confirmed that CSR implementation increases customer satisfaction and customer loyalty, which in turn enhance organisational performance. This paper discusses the managerial implications for container shipping firms to improve their CSR and organisational performance.

An analysis of institutional pressures, green supply chain management, and green performance in the container shipping context

This paper presents a study which utilized a conceptual framework with institutional theory as its base to empirically evaluate the impact of institutional pressures, internal green practices, and external green collaborations on green performance. Factor analysis was employed to identify the key institutional pressures (i.e. coercive, normative and mimetic pressures), internal green practices (i.e. green shipping practices and green operations), external green collaborations (i.e. green collaboration with supplier, green collaboration with partner, and green collaboration with customer), and green performance (i.e. reduction of pollutants, and perceived green brand) dimensions. We collected data from surveyees employed by 129 container shipping companies and agencies in Taiwan, and applied a structural equation model (SEM) to test the research hypotheses. The findings revealed that institutional pressures have positive effects on internal green practices; internal green practices positively influence external green collaborations; internal green practices and external green collaborations positively influence green performance but institutional pressure is not positively associated with external green collaborations. Theoretical contributions and managerial implications are presented to help container shipping operators improve green performance.

A liner shipping competitive model with consideration of service quality management

Under current competitive liner shipping market, it is crucial to explore the optimal shipping strategy for the subsistence and development of liner companies. In order to establish a liner shipping competitive model, we choose service quality, which can be measured by a range of unstructured data of relative items (such as delivery service, security, processing speed, user-friendliness) with big data analytics, as a key factor in the utility function and analyze the impact of service quality on the pricing strategy for container liner shipping context. By using the analytic hierarchy process, fuzzy comprehensive evaluation and time series forecasting method, the concrete data from South America container liner shipping market is analyzed via empirical study. The finding has demonstrated the model could yield management value for liner companies, and could provide theoretical guidance to formulate the optimal liner shipping strategy.

Assessment of differences in efficiency across strategic groups in the container shipping context: a data envelopment analysis

This study examines differences in efficiency across strategic groups in the container shipping context using data envelopment analysis of secondary data collected from 17 global container carriers. Global container carriers were classified based on strategic variables such as fleet capacity, asset/debt ratio, owned/chartered-in fleet ratio, non-container revenue ratio, and revenue. Four strategic groups were identified, namely: proactive-prudent, proactive-chance, conservative-prudent and conservative-chance. DEA results indicated significant differences in efficiency across the four strategic groups. The proactive-prudent group had significantly a higher fleet capacity, lower leverage and higher efficiency than the other strategic groups. Theoretical and managerial implications of the research findings are discussed to improve container carriers' operational efficiency.

Assessment of differences in efficiency across strategic groups in the container shipping context: a data envelopment analysis

Assessment of differences in efficiency across strategic groups in the container shipping context: a data envelopment analysis

Evaluating the use of alternative maritime power in Taiwan

PurposeThe purpose of this study is to empirically evaluate the impacts of institutional pressures on the use of alternative maritime power (AMP) and which in turn enhance environmental performance in the maritime shipping context.Design/methodology/approachFactor analysis was used to identify the key coercive pressure, normative pressure, mimetic pressure, AMP practice and green performance dimensions. The author collects data from a survey of 184 maritime shipping operators in Taiwan and applies a structural equation modelling (SEM) to test the research hypotheses.FindingsThe findings show that AMP practice act as mediator variables between institutional pressures and environmental performance.Originality/valueTheoretical contributions and managerial implications are drawn to help maritime shipping operators to improve environmental performance.