Maritime Transport Research Articles

Review: On the Waves of Empire: U.S. Imperialism and Merchant Sailors 1872–1924, by William D. Riddell

Review: <i>On the Waves of Empire: U.S. Imperialism and Merchant Sailors 1872–1924</i>, by William D. Riddell

Winds of change – Nomadic grassroots innovations in the maritime shipping sector

Grassroots innovations are seen as important contributors to sustainability transitions, however, given their dependence on place-specific conditions they face diffusion challenges. Responding to previously outlined gaps regarding conceptualisation and exploration of the spatiality of grassroots innovations, this paper introduces a conceptualisation of nomadic grassroots innovations. These are grassroots innovations that are mobile and perform activities in multiple places they continuously return to, conceptualised as host localities, while also being based in home localities containing for example offices and key infrastructure. Contributing to a more nuanced understanding of the geography of grassroots activities, the analysis explores the spatiality of socio-technical elements influencing three stages of development (initiation, operations, and upscaling and diffusion) of nine grassroots initiatives promoting a return to traditional sail cargo ships. Findings suggests that nomadic grassroots innovations are dependent on elements in home and host localities as well as from national and international contexts, indicating that the spatiality of nomadic grassroots innovations differs from place-based initiatives throughout all stages of development. Furthermore, analysis of challenges and opportunities for development of nomadic grassroots innovations reveals three key learnings for grassroots innovation diffusion: strategies for developing social cohesion through multi-scalar actor networks, utilising (costumer) demand for alternative solutions, and taking favourable sector conditions as a starting point when designing grassroots innovations. Combined, this could enable development of grassroots innovations that are less dependent on place-specific conditions and thereby easier to replicate, which could increase the capacity of grassroots innovations to tackle global challenges such as climate change.

Investigating temperature fluctuations in the wine and liquor maritime supply chain from South Africa to the United Kingdom: A case study

This study explored temperature fluctuations in dry shipping containers transporting wine and liquor along maritime supply chains. It also examined how these variations affect product quality, and evaluated the effectiveness of thermal foil container liners in mitigating temperature changes. Temperature trials were conducted from the South African loading depot and concluded at the first distribution centre in the United Kingdom. Ambient temperature sensors were placed on cartons of wine and liquor inside the thermal container liner and between the thermal liner and the door of the container. Weather data was also collected during the trials. Temperature profiles showed large fluctuations in temperature inside containers because of day and night cycles while the containers were stacked at the departure and destination ports, posing challenges for supply chain management. Descriptive statistics were used to analyse the temperature profiles, whereas logit analysis was used to determine the impact of the thermal container liner on the temperatures to which the wine and liquor were exposed. Blind tastings were used to evaluate product quality after exposure to temperature fluctuations. Results showed that thermal liners significantly reduced temperature variability, which is critical for maintaining wine and liquor quality. Despite this, blind tastings confirmed that some products were negatively affected by temperature deviations. The study recommends the use of thermal foil liners for maritime transport of wine and liquor to minimise product damage and financial losses. This finding is particularly relevant for exporters aiming to preserve the quality of their wine and liquor throughout long-distance shipping routes.

Reassessing the transition from paper to electronic bills of lading: regulatory impediments and prospective reforms in Nigeria’s maritime sector

Purpose The purpose of this paper is to re-evaluate the legal barriers surrounding the implementation of electronic billing systems and to propose potential reforms aimed at facilitating their efficient and widespread adoption in Nigeria. Since the 16th century, bills of lading have been integral to global trade, evolving in response to the practical exigencies and technological advancements of their respective eras. Despite their historical significance, the traditional paper-based bill of lading has increasingly been recognised as a hindrance to contemporary maritime commerce. The inherent vulnerabilities of physical documents such as susceptibility to damage and the logistical delays associated with their manual transfer have underscored the inefficiencies of this conventional system. These limitations have catalysed the development of electronic bills of lading (e-bills of lading) as a digital counterpart, a transition particularly pertinent within Nigeria’s maritime sector. Nevertheless, the adoption of e-bills of lading has engendered considerable apprehension, largely due to uncertainties surrounding their legal validity and operational efficacy. Design/methodology/approach This paper conducts a comprehensive desk-based study to examine the historical evolution and current concerns surrounding e-bills of lading as a substitute for traditional paper forms. Findings Specifically, the paper advocates for the enactment of clear and robust legislation that would grant e-bills of lading the same legal status as their paper counterparts. Originality/value This paper offers an original contribution to the academic and legal discourse on the subject, providing a novel analysis of the regulatory gaps while proposing practical recommendations for the effective adoption of e-bills of lading in Nigeria’s maritime industry.

Mitigating Maritime Cybersecurity Risks Using AI-Based Intrusion Detection Systems and Network Automation During Extreme Environmental Conditions

The maritime industry is increasingly confronted with a myriad of cybersecurity challenges exacerbated by extreme environmental conditions, technological advancements, and heightened reliance on automation. This review paper discusses the intersection of these factors, focusing on the adoption of artificial intelligence (AI)-based intrusion detection systems (IDS) and network automation as vital strategies for mitigating cybersecurity risks. The paper begins by outlining the unique cybersecurity threats faced by the maritime sector, which include data breaches, phishing attacks, and malware threats, all amplified by adverse weather and geographical isolation. In light of these challenges, the rationale for integrating AI-driven solutions into maritime operations is discussed. AI-based IDS can enhance threat detection capabilities through advanced machine learning algorithms that adapt to evolving cyber threats while minimizing false positives. Additionally, network automation can improve connectivity and data security, facilitating real-time monitoring and response to incidents. The review also addresses the critical need for collaboration between maritime and technology industries, emphasizing how partnerships can foster innovation and provide tailored solutions to the sector’s specific needs. Furthermore, the paper examines current implementations and case studies that illustrate successful applications of AI and automation in adverse maritime conditions. While recognizing the potential benefits, the review highlights the technical and operational challenges inherent in these implementations, including data integration, regulatory compliance, and cultural differences between sectors. Ultimately, this paper aims to provide a comprehensive overview of the state of maritime cybersecurity and the pivotal role of AI and automation in shaping a resilient, secure maritime future. The findings underscore the importance of ongoing research and development, collaborative efforts, and the necessity of adaptable strategies to safeguard maritime operations against the evolving landscape of cyber threats.

STAD: Ship trajectory anomaly detection in ocean with dynamic pattern clustering

With the continuous increase of maritime traffic, developing an efficient and accurate model for ship trajectory anomaly detection has become crucial for ensuring maritime transportation safety. The high complexity and variability of the marine environment lead to diverse ship trajectory patterns, making it challenging to learn effective trajectory representations for accurately identifying anomalies. We thus proposed an unsupervised deep learning model called STAD for ship trajectory anomaly detection in ocean to address this challenge. Concretely, STAD leverages offset reconstruction-based representation learning and a deep Gaussian Mixture Model (GMM) estimation network to learn the underlying complex clustering patterns of ship trajectories and utilize the learned patterns to enhance trajectory anomaly detection. Extensive experiments on multiple AIS datasets indicate that our model significantly outperforms existing methods in detecting multiple representative types of ship trajectory anomalies, including shift deviation, abnormal heading, and abnormal speeding. This study could help closely monitor the status of ship movement and detect abnormal behaviors in advance, thus benefiting maritime safety.

The effect of failure on energy efficiency in maritime vessels autopilot systems

The increasing demands for energy efficiency and environmental sustainability in the maritime industry have underscored the critical importance of optimizing autopilot systems, which, despite their significance, are often overlooked in ship energy efficiency management. The objective of this study is to enhance the energy efficiency of ships by focusing on the efficiency of autopilot systems, which play a significant role in the management of energy efficiency. The research emphasizes the need for effective decision support systems for ship operators, not only for optimizing ship speeds but also for making informed operational decisions. By utilizing Fuzzy Fault Tree Analysis (FFTA), the study identifies and prioritizes the causes of efficiency losses in autopilot systems and examines their frequency. Based on expert opinions, the research delves into the complexity of autopilot systems and the interactions among various components. Notably, the study highlights the impact of multiple factors on the efficiency of complex autopilot systems, elucidating their relationships through Minimum Cut Sets (MCS) analysis. Furthermore, attention was drawn to the “Improper Alarm Input” event caused by insufficient knowledge and awareness among ship operators, which hinders the effective use of autopilot systems. The findings demonstrate that decision support systems can increase energy efficiency and contribute to the reduction of operational errors by reducing the human factor, which is 99% effective on the “Inefficient Heading Control System”. Additionally, proper utilization of autopilot systems can lead to a decrease in a ship's carbon footprint and operating costs. Overall, the results can affect strategic decisions in ship energy efficiency management and encourage significant steps toward achieving International Maritime Organization's (IMO) sustainability objectives.

Digital Transformation in the Maritime Industry; Opportunities and Challenges for Indonesia

Digital transformation is reshaping industries worldwide, including the maritime sector, by integrating advanced technologies to improve efficiency, safety, and sustainability. In the context of Indonesia, a country with a vast archipelago and significant maritime activities, digital transformation offers substantial opportunities but also presents distinct challenges. This paper explores the multifaceted impacts of digital transformation on Indonesia's maritime industry, examining both the opportunities and challenges that arise from this technological shift. Opportunities in the maritime sector include enhanced operational efficiency, improved safety, and better environmental management. Technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI), and blockchain are pivotal in optimizing logistics, predictive maintenance, and real-time monitoring of vessels and cargo. These advancements can lead to reduced operational costs, minimized human errors, and lower environmental footprints. For Indonesia, leveraging these technologies could significantly boost its competitiveness in global shipping, enhance port operations, and support the sustainable management of its extensive marine resources. However, the journey towards digital transformation in Indonesia’s maritime industry is fraught with challenges. Key issues include inadequate digital infrastructure, limited technological expertise, and regulatory hurdles. The uneven distribution of digital infrastructure across Indonesia's archipelago hampers the consistent application of advanced technologies. Additionally, the maritime workforce requires upskilling to handle new digital tools effectively, necessitating substantial investment in education and training. Regulatory frameworks also need to evolve to address data security, privacy concerns, and the integration of international standards, ensuring a seamless transition to digital operations.

The Role of Business Incubators in Encouraging Maritime Technology Start-up Innovation

This study examines the critical role of business incubators in driving innovation in maritime technology start-ups. With the growing need for innovative technology solutions in the maritime industry, business incubators have emerged as a key catalyst in developing and introducing new ideas to the market. The study used a qualitative approach, involving in-depth interviews with 30 stakeholders, including start-up founders, incubator managers, and maritime industry experts. The results showed that business incubators provide critical support in the form of access to funding, expert mentoring, research and development facilities, and extensive industry networks. The findings also revealed that incubators that focus on the maritime sector are more effective in helping start-ups overcome industry-specific challenges. The study highlights the importance of collaboration between incubators, the maritime industry, and educational institutions to create an ecosystem that supports innovation. Implications of this study include recommendations for policymakers and industry players in designing more effective incubation programs to drive maritime technology innovation.

Assessing the environmental and economic viability of floating PV-powered green hydrogen: A case study on inland ferry operations in Türkiye

This paper conducts a comprehensive analysis of the environmental impacts and costs associated with a floating photovoltaic (FPV)-powered green hydrogen production system for a ferry line in Türkiye, utilizing life cycle assessment (LCA) and life cycle cost assessment (LCCA) methods. The methodology involves regridding daily data from 13 Global Climate Models (GCMs) to a consistent 1° × 1° grid, comparing this data with ERA5 datasets using various statistical methods, and identifying the top four GCMs. These models are then used to forecast ambient temperature, wind speed, and solar radiation for 2023–2052. Based on these forecasts, the FPV system's component sizes are determined. The LCA, based on the GREET 2022 database, reveals that green hydrogen is the most environmentally friendly option, reducing global warming potential (GWP) by 77.5 % compared to marine diesel oil (MDO 0.1 % S), with more than 62 % of GWP for the hydrogen-powered ferry attributed to PV production. LCCA results indicate that, without subsidies, green hydrogen is not economically feasible for the selected inland ferry line. Carbon taxes are insufficient to bridge cost differences, highlighting the need for financial incentives. Reducing corporate tax to 10 % is identified as the most effective incentive, and the Production Tax Credit (PTC) option maximizes internal rates of return for green hydrogen producers. This study provides valuable insights for sustainable energy choices in maritime transport, emphasizing the importance of both environmental and economic considerations.

Evaluation of the powering extrapolation of a ship with a gate rudder system, including ageing and fouling effects

This paper is based on the evaluation results of the calm water powering extrapolation of a 90-m general cargo vessel, originally built with a conventional rudder system (CRS) and later retrofitted with an innovative energy-saving device (ESD) known as the Gate Rudder System® (GRS), as part of the EU H2020 Project GATERS. The power estimation of the vessel was conducted using an adapted extrapolation procedure based on the scaled model tests of the vessel in a towing tank, validated by the three sets of sea trials. The first sea trial was performed when the ship was new, while the latter two were conducted during the project timeline, before and after the GRS retrofit. This paper aims to adapt the ITTC-78 power extrapolation method to predict the power performance of a ship retrofitted with the GRS for the first time in the maritime industry. Within this framework, the specific objectives of the paper are to describe and validate the proposed method using relevant power-speed data of the GATERS project target vessel (M/V ERGE) initially equipped with the CRS and later replaced by the retrofit GRS to achieve the project objective. Due to the real-world circumstances of retrofitting a 13-year-old ship with a novel energy-saving device, investigating the effects of ageing, hull roughness, and fouling has also become one of the key objectives of the paper. The findings demonstrate the ageing effect on the target ship's powering performance after retrofitting with GRS, highlighting the need for special consideration of power extrapolation alongside the hull fouling effects. The paper also demonstrates that the GRS is an emerging ESD, as a retrofit beside newly built ships with GRS, which improved the powering performance of the project target vessel, achieving a remarkable 25% power saving based on the comparative full-scale sea trials.

Research on the spatial distribution and influencing factors of the maritime services: based on data from Chinese enterprises

ABSTRACT In the context of global maritime cluster development and the strategic evolution of port cities, the spatial distribution and determinants of Maritime Services (MSs) emerge as pivotal areas of inquiry. However, empirical analysis in this domain remains limited. To address this deficiency, our study meticulously processes and analyses data from Chinese Maritime Service Enterprises (MSEs) listed in the World Shipping Register Company until 2023, categorizing these enterprises into port-related, ship-related and maritime consulting enterprises. Utilizing instrumental variables and econometrics, our findings reveal that MSs in China predominantly aggregate in coastal cities, along major inland waterways, and within inland central cities, with a pronounced clustering near urban centers. This distribution is primarily influenced by two factors: firstly, geographic proximity to seaports and throughput levels markedly impacts the distribution, especially for port-related enterprises and ship-related enterprises. Secondly, the clustering of downstream supporting industries, shared labor pools and knowledge spillovers, which are essential elements of agglomeration economics, particularly affects maritime consulting enterprises. Such a pattern is also influenced by the broader business environment and governmental governance. This research not only clarifies existing disputes regarding the maritime industry’s spatial distribution but also offers insights for MSs development and the transformative planning of port cities.

Spatiotemporal Analysis of Complex Emission Dynamics in Port Areas Using High-Density Air Sensor Network.

Cargo terminals, as pivotal hubs of mechanical activities, maritime shipping, and land transportation, are significant sources of air pollutants, exhibiting considerable spatiotemporal heterogeneity due to the complex and irregular nature of emissions. This study employed a high-density air sensor network with 17 sites across four functional zones in two Shanghai cargo terminals to monitor NO and NO2 concentrations with high spatiotemporal resolution post sensor data validation against regulatory monitoring stations. Notably, NO and NO2 concentrations within the terminal surged during the night, peaking at 06:00 h, likely due to local regulations on heavy-duty diesel trucks. Spatial analysis revealed the highest NO concentrations in the core operational areas and adjacent roads, with significantly lower levels in the outer ring, indicating strong emission sources and limited dispersion. Employing the lowest percentile method for baseline extraction from high-resolution data, this study identified local emissions as the primary source of NO, constituting over 80% of total emissions. Elevated background concentrations of NO2 suggested a gradual oxidation of NO into NO2, with local emissions contributing to 32-70% of the total NO2 concentration. These findings provide valuable insights into the NO and NO2 emission characteristics across different terminal areas, aiding decision-makers in developing targeted emission control policies.

Statistical seasonal prediction of Arctic sea ice concentration based on spatiotemporal anomaly persistent method

Abstract Accurate prediction of Arctic sea ice is crucial for high-latitude and even mid-latitude climate prediction. It significantly affects atmospheric circulation, the environment, ecology, and maritime transport. This study developed a statistical prediction model to predict monthly Arctic sea ice concentration (SIC) for up to one year based on the season-reliant empirical orthogonal functions (SEOFs) technique. Its prediction skill was compared with that of a dynamical prediction model. The spatiotemporal pattern of sea ice anomalies, which exhibit strong seasonality and are maintained for a significant period above the seasonal time scale by atmosphere-ocean interactions, was extracted using SEOFs. A prediction model was constructed by extrapolating from the recent anomalous state of sea ice to predict the future. Experimental retrospective predictions with monthly time resolution for 1982–2021 were performed to validate the prediction skill of Arctic SIC and areal extent. Statistically significant prediction skills were achieved over several months, even up to six months, exceeding the skill of the dynamical model.

Global Freight Transport Emissions Responsibility.

The transportation of freight by land, sea and air underpins the complex network of global trade in physical commodities. Greenhouse gas emissions from freight transportation are a significant component of global emissions and are predicted to grow in coming decades. However, the inclusion of freight transport in emissions accounts and environmental impact studies is often incomplete. Both data availability and difficulties in allocating freight emissions to specific commodity trades contributes to this. In this study, international freight movements by transport mode are estimated from the bottom-up by imputing global freight transport routes. Emissions are estimated from these freight movements and integrated with a global multiregional input-output model. This enables the calculation of carbon footprints that are complete with respect to freight emissions. We estimate that global freight transport emissions contributed 2.8 Gt CO2-equiv in 2012, or about 41% of total transport emissions. In general, freight footprints contribute about 9% to national emissions footprints. While trade in physical commodities (such as construction materials, food and fossil fuels) are associated with the largest embodied freight emissions, services (such as public administration, education and health) also require significant freight transport. Using a consumption-based allocation of freight transport emissions allows the decarbonisation of other sectors to be complementary to the decarbonisation of transport through reduction in demand, for example through material efficiency strategies. To drive decarbonisation in maritime transport it is critical to include bunker emissions in national emissions inventories, thereby completing the system boundary.

Experimental Modelling of Water-Wave Interactions with a Flexible Beam

A series of fluid-structure-interaction (FSI) experiments is presented for studying water-wave interactions with a flexible beam in a wide range of sea conditions, thereby yielding a repository of FSI test-case data. The aim is to use these experimental data in order to validate FSI solvers commonly employed by the maritime industry in the design of fixed-foundation, offshore wind turbines. The experimental set-up allows simultaneous measurements of beam deflections and their effect on incident and reflected waves. In addition, the study is carried out in a wide range of sea conditions ranging from regular-to-irregular and moderate-to-extreme wave height and steepness. The study of such a wide range of conditions makes the experiments suitable for providing reliable data in the validation of a suite of mathematical and numerical FSI solvers, i.e., linear, nonlinear and high-fidelity. The data from the experiments are made publicly available through open-source data-sharing platforms.

TG-PGAT: An AIS Data-Driven Dynamic Spatiotemporal Prediction Model for Ship Traffic Flow in the Port

Accurate prediction of ship traffic flow is essential for developing intelligent maritime transportation systems. To address the complexity of ship traffic flow data in the port and the challenges of capturing its dynamic spatiotemporal dependencies, a dynamic spatiotemporal model called Temporal convolutional network-bidirectional Gated recurrent unit-Pearson correlation coefficient-Graph Attention Network (TG-PGAT) is proposed for predicting traffic flow in port waters. This model extracts spatial features of traffic flow by combining the adjacency matrix and spatial dynamic coefficient correlation matrix within the Graph Attention Network (GAT) and captures temporal features through the concatenation of the Temporal Convolutional Network (TCN) and Bidirectional Gated Recurrent Unit (BiGRU). The proposed TG-PGAT model demonstrates higher prediction accuracy and stability than other classic traffic flow prediction methods. The experimental results from multiple angles, such as ablation experiments and robustness tests, further validate the critical role and strong noise resistance of different modules in the TG-PGAT model. The experimental results of visualization demonstrate that this model not only exhibits significant predictive advantages in densely trafficked areas of the port but also outperforms other models in surrounding areas with sparse traffic flow data.

Resource allocation choices in asset‐intensive industries

AbstractThe purpose of this study is to examine the determinants of tangible and intangible resource allocation in asset‐intensive organizations, by employing a qualitative and a quantitative study in the setting of the maritime shipping industry. First, the study employs seven in‐depth interviews to identify the firm‐specific factors that shape resource allocation decision‐making. Second, the study utilizes a unique panel dataset of public maritime shipping companies for the period 2010–2020 to empirically investigate the identified determinants of resource allocation choices. The results indicate that factors, such as firm's risk‐seeking profile, smaller size, limited cash liquidity, capability to access capital, lower cost of equity capital, higher cost of debt capital, lower level of insiders' ownership, younger CEO, and limited environmental‐related disclosures are positively affecting the number of tangible resources allocated. The findings, apart from enriching the body of literature, have some managerial implications that are discussed herein.

Strategies to improve ammonia combustion in a dual fuel marine engine by using CFD

Maritime transportation is currently responsible for delivering most of the world trade, and it still plays a major role in the global economy and pollutant emissions. Due to their heavy loads and long distances, full electrification is generally assumed as an unsuitable pathway, leaving combustion engines as the dominant technology in the marine sector. For a long time, these engines have benefited from the low costs of heavy oils produced from oil refining, but these fuels present a difficult combustion and are associated with significant pollutant emissions and greenhouse gases. Today, carbon-free fuels such as hydrogen and ammonia are gradually getting a foothold in the shipping market.In the present paper, an investigation on several strategies to improve the oxidation of ammonia is carried out for a dual fuel (DF) medium speed marine engine. A CFD analysis using ANSYS FORTE® code is performed on an improved and more realistic bowl geometry by varying the premixed fuel according to methods validated in previous studies and introducing adequate kinetic mechanisms from the literature. Results show that the sole use of ammonia to replace natural gas, as premixed fuel, leads to incomplete combustion; the increase of diesel fuel can only mitigate this condition, but it cannot be considered as viable solution. Therefore, several blends of ammonia/hydrogen are tested to examine combustion development and pollutant emissions. The addition of hydrogen above 10% to the premixed charge composition demonstrates to promote the oxidation of ammonia and to effectively reduce CO2 emissions of 84% without a dramatic increase in nitrogen oxides. On the contrary, hydrogen percentages lower than 10% feature a worsening of the combustion of the premixed charge. Finally, the use of a fuel containing nitrogen promote a slight formation of NOx emissions.

AI-Driven Predictive Maintenance in Modern Maritime Transport—Enhancing Operational Efficiency and Reliability

AI-Driven Predictive Maintenance in Modern Maritime Transport—Enhancing Operational Efficiency and Reliability