Search Results (890)

As port authorities and cargo operators seek strategies to reduce carbon emissions while ensuring operational efficiency, some are turning to the concept of green corridors. These solutions aim to establish formalized partnerships among ports, carriers, shippers, and countries. During the process, the stakeholders must consider four priority areas (alternative fuels, bunkering infrastructure, vessel decarbonization pathways, and cargo demand dynamics) from seven angles (environmental, economic, infrastructure, regulatory, operational, technological, and social). This study explores the prioritization of these criteria for establishing a green maritime corridor between two major ports in Portugal and Angola, which would be a significant step toward promoting sustainable global trade. Utilizing the fuzzy AHP, this research analyzes all these factors and their associated sub-criteria derived from a comprehensive literature review and consultations with stakeholders from the Ports of Sines and Luanda. The findings show the dominance of environmental compatibility and economic viability, while social acceptance shows the lowest score. This framework guides the decision-making process for developing a sustainable shipping corridor. The results offer valuable insights for policymakers which can guide them in fostering resilient maritime transport routes, accelerating the adoption of decarbonization strategies and playing a critical role in achieving the IMO’s zero-emission targets by 2050. Full article

Reducing maritime greenhouse gas (GHG) emissions is challenging. As efforts to address climate change are gaining momentum, reducing the environmental impact becomes crucial for maritime short-to-medium-term sustainability. The International Maritime Organisation (IMO) has adopted Life Cycle Assessment (LCA) guidelines for estimating GHG emissions associated with alternative fuels. This paper proposes an examination of the latest IMO-adopted LCA guidelines, comparing them with existing methodologies used for the transport sector. By scrutinising these guidelines, the paper aims to provide a better understanding of the evolving landscape for GHG emission estimation within the maritime sector. The paper presents a case study that applies the newly established LCA guidelines to a promising alternative fuel pathway, i.e., waste-wood-derived pyrolysis oil. Pyrolysis oil offers an attractive option, leveraging waste materials to generate a sustainable energy source. The environmental impact of pyrolysis oils is quantified according to the IMO LCA guidelines, offering insights into its viability as a cleaner alternative as marine fuel. The results show the large potential for GHG savings offered by this pathway: upgraded pyrolysis oil can deliver significant GHG savings, and this contribution is linearly dependent of its energy share when blended with standard Heavy Fuel Oil. Full article

This study examines the use of Unmanned Aerial Vehicles (UAVs) in transportation, focusing on traffic monitoring and accident prevention. UAVs provide a cost-effective means for traffic surveillance, route planning, and accident analysis, enhancing data accuracy and timeliness. The paper discusses autonomous and human-intervention-supported drone systems for traffic surveillance, addressing technological and operational challenges and the balance needed for practical implementation. It also presents recent advancements, including a forerunner drone model, and references research on UAVs for maritime navigation safety, underscoring the need for their safe and efficient integration into transportation systems. Full article

Increased maritime cargo transportation has necessitated stricter management of emissions from ships. The primary source of this pollution is fuel combustion, which is influenced by factors such as a ship’s added wave resistance. Accurate estimation of this resistance during ship design is crucial for minimizing exhaust emissions. The challenge is that, at the preliminary parametric design stage, only limited geometric data about the ship is available, and the existing methods for estimating added wave resistance cannot be applied. This article presents the application of artificial neural network (ANN) ensembles for estimating added wave resistance based on dimensionless design parameters available at the preliminary design stage, such as the length-to-breadth ratio (L/B), breadth-to-draught ratio (B/T), length-to-draught ratio (L/T), block coefficient (CB), and the Froude number (Fn). Four different ANN ensembles are developed to predict this resistance using both complete sets of design characteristics (i.e., L/B, B/T, CB, and Fn) and incomplete sets, such as L/B, CB, and Fn; B/T, CB, and Fn; and L/T, CB, and Fn. This approach allows for the consideration of CO₂ emissions at the parametric design stage when only limited ship dimensions are known. An example in this article demonstrates that minor modifications to typical container ship designs can significantly reduce added wave resistance, resulting in a daily reduction of up to 2.55 tons of CO₂ emissions. This reduction is equivalent to the emissions produced by 778 cars per day, highlighting the environmental benefits of optimizing ship design. Full article

High-density polyethylene (HDPE) has emerged as a promising alternative to fiber-reinforced plastic (FRP) for small vessel manufacturing due to its durability, chemical resistance, lightweight properties, and recyclability. However, while thermoplastic polymers like HDPE have been extensively used in gas and water pipelines, their application in large, complex marine structures remains underexplored, particularly in terms of joining methods. Existing techniques, such as ultrasonic welding, laser welding, and friction stir welding, are unsuitable for large-scale HDPE components, where extrusion welding is more viable. This study focuses on evaluating the impact of key process parameters, such as the preheating temperature, hot air movement speed, and nozzle distance, on the welding performance of HDPE. By analyzing the influence of these variables on heat distribution during the extrusion welding process, we aim to conduct basic research to derive optimal conditions for achieving strong and reliable joints. The results highlight the critical importance of a uniform temperature distribution in preventing defects such as excessive melting or thermal degradation, which could compromise weld integrity. This research provides valuable insights into improving HDPE joining techniques, contributing to its broader adoption in the marine and manufacturing industries. Full article

Fishery plays an important role in world trade; however, the embodied energy associated with fishery remains incompletely quantified. In this study, we applied the multi-regional input-output (MRIO) model and logarithmic mean Divisia index (LMDI) approach to understand the evolution and drivers of embodied energy in the intermediate and final fishery trade between China and countries along the 21st century Maritime Silk Road (MSR) from 2006 to 2021. The findings are as follows: (1) Embodied energy in the intermediate fishery trade averaged 92.2% of embodied energy from the total fishery trade. China has gradually shifted from being a net exporter to a net importer of embodied energy in intermediate, final, and total fishery trade with countries along the MSR. (2) From a regional perspective, the embodied energy in China’s fishery trade with Japan, South Korea, and Southeast Asia comprises the majority of the embodied energy from China’s total fishery trade (82.0% on average annually). From a sectoral perspective, petroleum, chemical and non-metallic mineral products, and transport equipment were prominent in the embodied energy of China’s intermediate fishery trade (64.0% on average annually). (3) Economic output increases were the main contributors to the increasing embodied energy in all types of fishery trade in China. The improvement in energy efficiency effectively reduced the embodied energy in all types of fishery trade in China, but its negative driving force weakened in recent years owing to minor energy efficiency improvements. Understanding the embodied energy transactions behind the intermediate and final fishery trade with countries along the MSR can provide a theoretical reference for China to optimize its fishery trade strategy and save energy. Full article

This article examines the methods and application of classification trees and multi-criteria decision-making in the process of holding offshore vessels in port (Port State Control—PSC). This work aims to improve the efficiency and precision of the control processes in the detention of offshore vessels by using advanced analytical methods. Methodologically, a classification decision tree was used to identify the most important risk factors, enabling a faster and more accurate assessment of the possibility of detaining offshore vessels in port. Multi-criteria decision-making (MCDM) also enabled the simultaneous assessment of multiple factors, ensuring a balanced, robust, accurate, and objective approach. The research results show that the integration of these methods into the PSC process can significantly increase the safety of shipping and reduce the operating costs of offshore vessels. The application of these analytical tools can lead to a more systematic and transparent inspection process. This paper suggests further research and training of inspectors in the use of these techniques to maximize their applicability and effectiveness. Finally, this paper emphasizes the potential of classification trees and MCDM for safer and more efficient maritime transport by improving PSC procedures. Full article

This study examines the significant impact of Houthi insurgent activities on maritime traffic within the strategic Red Sea and Suez Canal routes, essential conduits for global trade. It explores the correlation between regional instability, exemplified by Houthi actions from 19 November 2023 to 5 February 2024, and changes in maritime traffic patterns and operational efficiency. This study seeks to answer a critical question in transport geography: how does regional instability, exemplified by Houthi insurgent activities, affect the maritime traffic patterns and operational efficiency of the Red Sea and Suez Canal? Using descriptive statistics, qualitative analysis, and geospatial methods, this research highlights recent trends in maritime traffic and incidents, revealing spatial and geopolitical challenges in this crucial trade route. The findings indicate a notable decline in maritime activity in the Gulf of Aden and Suez Canal due to security concerns from Houthi attacks, prompting a significant shift to alternative routes, particularly around the Cape of Good Hope. This shift underscores the broader implications of regional instability on global trade and the importance of maintaining an uninterrupted maritime flow. This study also emphasizes the economic ramifications, such as increased operational costs and freight rates due to longer transit times and enhanced security measures. This research concludes with a call for improved maritime security protocols and international cooperation to protect these strategic maritime pathways. It contributes to the discourse on transport geography by quantifying the direct impacts of regional conflicts on maritime logistics and proposing strategies for future resilience, highlighting the interconnected nature of global trade and security and the need for collective action against evolving geopolitical challenges. Full article

Towing cable arrays have made significant contributions across various fields, and their outspread process is crucial for realizing their functionalities. However, research on the dynamic characterization of the outspread process of towed cable arrays lacks systematic organization. This paper reviews, organizes, and analyzes the outspread process of towing cable arrays, drawing on relevant models, case studies, and structural features. It ingeniously applies concepts from parachute outspread to the analysis of towing-cable-array deployment. The study systematically examines the deployment of towing cable arrays under varying cable lengths, wave conditions, and the interactions between line arrays. The goal is to integrate existing research on the outspread of towing cable arrays, addressing the gaps in the description of this process and providing a comprehensive analysis of the outspread characteristics under different conditions. Additionally, this paper identifies current limitations in this area and provides insights for future developments. Furthermore, it explores the potential application of AI to address these challenges. The aim of this paper is to contribute meaningfully to this field. Full article

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. Full article

Accurate pedestrian trajectory prediction is crucial in many fields. This requires the full use and learning of pedestrians’ social interactions, movements, and environmental information. In view of the current research on pedestrian trajectory prediction, wherein most of the pedestrian interaction information is explored from the level of overall interaction, this paper proposes the SISGAN model, which designs a social interaction module from the perspective of the target pedestrian, and takes four kinds of interaction information as the influencing factors of pedestrian interaction, so as to describe the influence mechanism of pedestrian–pedestrian interaction. In addition, in terms of environmental information, the index density of pedestrian historical trajectory in space is taken into account in the extraction of environmental information, which increases the potential correlation between environmental information and pedestrians. Finally, we integrate social interaction information and environmental information and make the final trajectory prediction based on GAN. Experiments on ETH and UCY datasets demonstrate the effectiveness of the SISGAN model proposed in this paper. Full article

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. Full article

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. This paper presents a novel approach to predictive maintenance in the maritime industry, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance fault detection and maintenance planning for naval systems. Traditional maintenance strategies, such as corrective and preventive maintenance, are increasingly ineffective in meeting the high safety and efficiency standards required by maritime operations. The proposed model integrates AI-driven methods to process operational data from shipboard systems, enabling more accurate fault diagnosis and early identification of system failures. By analyzing historical operational data, ML algorithms identify patterns and estimate the functional states, helping prevent unplanned failures and costly downtime. This approach is critical in environments where technical failures are a leading cause of incidents, as demonstrated by the high rate of machinery-related accidents in maritime operations. Our study highlights the growing importance of AI and ML in predictive maintenance and offers a practical tool for improving operational safety and efficiency in the naval industry. The paper discusses the development of a fault detection approach, evaluates its performance on real shipboard data-through tests on a seawater cooling system from an oil tanker and concludes with insights into the broader implications of AI-driven maintenance in the maritime sector. Full article

In this article, an extension of a conventional partial discharge (PD) approach called partial discharge echo (PDE), which is applied to different classes of electrical insulation systems of power devices, is presented. Currently, high-voltage (HV) electrical insulation is attributed not only to transmission and distribution grids but also to the industrial environment and emerging segments such as transportation electrification, i.e., electric vehicles, more-electric aircraft, and propulsion in maritime vehicles. This novel PDE methodology extends the conventional and established PD-based assessment, which is perceived to be one of the crucial indicators of HV electrical insulation integrity. PD echo may provide additional insight into the surface conditions and charge transport phenomena in a non-invasive way. It offers new diagnostic attributes that expand the evaluation of insulation conditions that are not possible by conventional PD measurements. The effects of partial discharge echo in various segments of insulation systems (such as cross-linked polyethylene [XLPE] power cable sections that contain defects and a twisted-pair helical coil that represents motor-winding insulation) are shown in this paper. The aim is to demonstrate the echo response on representative electrical insulating materials; for example, polyethylene, insulating paper, and Nomex. Comparisons of the PD echo decay times among various insulation systems are depicted, reflecting dielectric surface phenomena. The presented approach offers extended quantitative assessments of the conditions of HV electrical insulation, including its detection, measurement methodology, and interpretation. Full article

Artificial intelligence aims to be the solution to multiple engineering problems by trying to emulate the human learning process. In this sense, maritime transport standards have clearly evolved, which are based on two principal pillars: the International Convention for the Safety of Life at Sea Convention (SOLAS) and the International Convention for the Prevention of Pollution from Ships (MARPOL). Based on a formal safety assessment research process, these pillars try to solve most of the maritime transport accidents, which, in their final steps, are associated with human factors. In this research, an original methodology employing a deep learning process for image recognition during mooring line operation, a dangerous process on ships, is developed. The main results indicate that the proposed method is an excellent tool for advising ship officers on watch and, consequently, provides a new way to prevent human factors onboard from causing accidents, which in the future must be considered in international standards. Full article