Search Results (58)

This paper presents a cloud-based architecture for the acquisition, transmission, and processing of acoustic data from hydrophone arrays, designed to enable the detection and monitoring of both surface and underwater vehicles. The proposed system offers a modular and scalable cloud infrastructure that supports real-time and distributed processing of hydrophone data collected in diverse aquatic environments. Acoustic signals captured by heterogeneous hydrophones—featuring varying sensitivity and bandwidth—are streamed to the cloud, where several machine learning algorithms can be deployed to extract distinguishing acoustic signatures from vessel engines and propellers in interaction with water. The architecture leverages cloud-based services for data ingestion, processing, and storage, facilitating robust vehicle detection and localization through propagation modeling and multi-array geometric configurations. Experimental validation demonstrates the system’s effectiveness in handling high-volume acoustic data streams while maintaining low-latency processing. The proposed approach highlights the potential of cloud technologies to deliver scalable, resilient, and adaptive acoustic sensing platforms for applications in maritime traffic monitoring, harbor security, and environmental surveillance. Full article

The maritime transport industry contributes around 3% to worldwide CO₂ emissions, with 2023 emissions projected to be approximately 58 billion tons. Consequently, to attain decarbonization objectives, the implementation of effective reduction measures in maritime operations, especially at seaports as significant contributors, is essential. On the other hand, seaport operations are categorized into two main areas: land logistics, encompassing cargo handling, storage, customs processing, and inland transportation, and maritime logistics, which includes vessel traffic management, berth allocation, cargo loading and unloading, and fuel and maintenance services. While land logistics’ decarbonization has been extensively studied, maritime logistics operations, accounting for about 60% of port CO₂ emissions, remain underexplored. Their progress relies on regulations, cleaner fuels, and digital solutions; yet high costs and slow adoption pose significant challenges. As a result, this study employed PRISMA-ScR methodology to select relevant research resources and validate global reports from international organizations, enhancing transparency and providing practitioners and experts with a comprehensive analysis of seaport maritime emissions, as well as decarbonization initiatives. This study analyzes the future trajectory of the initiative based on current data, evaluating its potential benefits and systematically reviewing recent literature. It explores decarbonization strategies in maritime operations, emphasizing regulations, cleaner fuels, and digital solutions while highlighting challenges such as high costs and slow adoption. Key issues examined include maritime border delineation, infrastructure constraints, technological advancements, regulatory barriers, and the opportunities that decarbonized seaports offer to ports and their surrounding regions. Full article

As ship traffic volumes increase and navigable waters become more complex, vessel traffic service operators (VTSOs) face growing challenges to effectively monitor marine traffic. To address the heavy reliance on human expertise in current ship supervision, we propose a method for quickly identifying high risk ships to enhance the situational awareness of VTSOs in complex waters. First, the K-means clustering algorithm is improved using the Whale Optimization Algorithm (WOA) to adaptively cluster ships within a waterway, segmenting the traffic in the area into multiple ship clusters. Second, a ship cluster collision risk assessment model is developed to quantify the degree of collision risk for each ship cluster. Finally, a weighted directed complex network is constructed to identify high risk ships within each ship cluster. Experimental simulations show that the proposed WOA–K-means clustering algorithm outperforms other adaptive clustering algorithms in terms of computation speed and accuracy. The developed ship cluster collision risk assessment model can identify high risk ship clusters that require VTSO attention, and the weighted directed complex network model accurately identifies high risk ships. This approach can assist VTSOs in executing a comprehensive and targeted monitoring process encompassing macro, meso, and micro aspects, thus boosting the efficacy of ship oversight, and mitigating traffic hazards. Full article

The development of China’s digital waterways has led to the extensive deployment of cameras along inland waterways. However, the limited processing and utilization of digital resources hinder the ability to provide waterway services. To address this issue, this paper introduces a novel waterway perception approach based on an intelligent navigation marker system. By integrating multiple sensors into navigation markers, the fusion of camera video data and automatic identification system (AIS) data is achieved. The proposed method of an enhanced one-stage object detection algorithm improves detection accuracy for small vessels in complex inland waterway environments, while an object-tracking algorithm ensures the stable monitoring of vessel trajectories. To mitigate AIS data latency, a trajectory prediction algorithm is employed through region-based matching methods for the precise alignment of AIS data with pixel coordinates detected in video feeds. Furthermore, an augmented reality (AR)-based traffic situational awareness framework is developed to dynamically visualize key information. Experimental results demonstrate that the proposed model significantly outperforms mainstream algorithms. It achieves exceptional robustness in detecting small targets and managing complex backgrounds, with data fusion accuracy ranging from 84.29% to 94.32% across multiple tests, thereby substantially enhancing the spatiotemporal alignment between AIS and video data. Full article

This study addresses the critical challenge of lacking quantitative measures for objective evaluation of vessel traffic service (VTS) operator workload, where current uniform staffing approaches fail to reflect center-specific operational characteristics. The VTS Operator Workload Index (VOWI) model was developed using the Delphi–AHP methodology to determine the relative importance of key factors including traffic, sea area characteristics, port facilities, and weather conditions, which formed the basis for calculating both center-wide and per-operator workload indices. Factor analysis revealed that traffic factors showed the highest importance at 0.4627, followed by sea area (0.1960), port facilities (0.1916), and weather (0.1497) factors. Application of the VOWI model to 19 VTS centers in South Korea demonstrated that per-operator workload at Busan, Incheon, and Ulsan VTS was up to three times higher than at other centers. This finding indicates that the current uniform staffing approach based on sector count inadequately reflects each center’s actual operational characteristics. The VOWI model provides objective criteria for efficient personnel management in VTS centers and is expected to contribute to improving VTS service quality. Full article

Vessel Traffic Services (VTS) systems are complex systems facilitating decision-making processes and integrating technical infrastructure, aiming to ensure the safety of ship traffic and marine environment protection in indicated water areas. Such services are offered in Poland in selected regions. These systems operate based on guidelines established by the International Maritime Organization (IMO) and European Parliament; therefore, they should be constantly developed and adjusted to current regulations. The aim of this article is to review and assess the adjustment of VTS systems operating in Poland to current selected regulations introduced by the IMO and European Parliament. A comparative analysis and evaluation of three VTS systems operated in Poland was carried out. In addition, the impact of VTS systems on the development of the trans-European transport network was examined. It was stated that the investigated VTS systems’ current adjustment to analyzed regulations is different depending on the systems’ configuration and possessed infrastructure, parameters of fairways, traffic regulations and other criteria. Based on the achieved research results, recommendations to improve the VTS systems in Poland were proposed. The research outcomes may be interesting for the managers of maritime administrations, ports’ authorities, and other decision-makers responsible for safe navigation and traffic management. Full article

Waterway intersections pose significant risks for vessel navigation due to the complexities of operational conditions in these areas. The lack of clear collision avoidance rules, combined with ineffective communication, exacerbates these dangers. To address this issue, transportation authorities will typically employ flow organization strategies to optimize operations at these intersections. However, effective methods for traffic management, both before and after implementation, are still lacking. This paper proposes a methodology to determine the level of service (LOS) needed for waterway intersections by using the degree of conflict during vessel navigation as a performance measure, while also considering the unique characteristics of vessel encounters in these areas. The methodology was applied to analyze the Yuxingnao waterway, and the results demonstrate its effectiveness in assessing operational conditions and providing a clear classification of service levels over specific time periods. Consequently, this methodology not only enables transportation authorities to evaluate the effectiveness of traffic management strategies, such as route planning and traffic organization, but also helps predict the impact of potential improvement countermeasures. Full article

Anchor dragging at anchorages poses a significant threat to marine traffic, potentially leading to collisions and damage to seabed infrastructure. This study analyzed a large dataset of ships in anchorage areas to develop a machine learning (ML) model that estimates the risk of anchor dragging using a binary classification system that differentiates between dragging and non-dragging incidents. Historical data from the automatic identification system (AIS), hydrographic, and meteorological sources were compiled for each case. Preliminary analysis revealed a significant class imbalance, with non-dragging cases far outnumbering dragging cases. This suggested that the optimal ML strategy would involve undersampling the majority class and cost-sensitive learning. A combination of data-undersampling methods and cost-sensitive algorithms was used to select the model with the best recall, area under the receiver operating characteristic curve (AUC), and geometric mean (GM) scores. The neighborhood cleaning rule undersampler paired with cost-sensitive logistic regression outperformed other models, achieving recall, GM, and AUC scores of 0.889, 0.767, and 0.810, respectively. This study also demonstrated potential applications of the model, discussed its limitations, and suggested possible improvements for the ML approach. Our method advances maritime safety by enabling the intelligent, risk-aware monitoring of anchored vessels through machine learning, enhancing the capabilities of vessel traffic service officers. Full article

This study aims to improve vessel trajectory prediction in the inner harbor of Busan Port using Automatic Identification System (AIS) data and deep-learning techniques. The research addresses the challenge of irregular AIS data intervals through linear interpolation and focuses on enhancing the accuracy of predictions in complex port environments. Recurrent neural network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU models were developed, with LSTM delivering the highest performance. The primary scientific question of this study is how to reliably predict vessel trajectories under varying conditions in inner harbors. The results demonstrate that the proposed method not only improves the precision of predictions but also identifies critical areas where Vessel Traffic Service Operators (VTSOs) can better manage vessel movements. These findings contribute to safer and more efficient vessel traffic management in ports with high traffic density and complex navigational challenges. Full article

This study was aimed at improving the energy efficiency of the distribution of liquefied natural gas (LNG) as shipping fuel in the southern Baltic Sea. The objective of this study was to determine the demand for LNG for maritime shipping by analyzing the distribution of the resource from the water side (ship-to-ship). LNG was chosen due to the location of the LNG terminal in Świnoujście within the analyzed water area, where a problem has arisen in the southern part of the Baltic Sea regarding fuel supply for vessels due to the lack of developed infrastructure along the coast. An analysis was conducted to optimize the size of the LNG fleet and infrastructure facilities. Seeking compliance with Annex VI to the MARPOL 73/78 Convention, adopted by the International Maritime Organization (IMO), shipowners see potential in the switch from conventional fuels to LNG. As one of the alternative solutions, it will contribute to reducing harmful emissions. Determination of the LNG distribution volume requires the identification of LNG storage facility locations, specifying the number of LNG-powered ships (broken down by type) and the number of LNG bunkering ships. The first part of this study contains a detailed analysis of the number of sea-going ships that provide services in the southern part of the Baltic Sea and the world’s number of LNG bunkering ships. The database contains a set of the characteristics required to determine the optimal demand for LNG, where LNG bunkering vessels are capable of supplying fuel within the shortest possible time and covering the shortest possible distance to LNG-powered ships. The characteristics include the type of ship, requested LNG volume, the speed of LNG bunkering ships, the distance between LNG facilities, and the loading rate (the volume of fuel received per time unit). Based on the collected data, the volume of LNG distribution was determined using MATLAB R2019a software. The remainder of this study contains a description of the conducted research and results of an analysis of the traffic density in the Baltic Sea. The results were obtained on the basis of data from the Statistical Yearbook of Maritime Economy and IALA IWRAP Mk2 2020 software. The number of LNG-powered ships and number of LNG bunkering ships were specified, and the demand for LNG for the area under analysis was determined. Full article

This study aims to identify the key success factors for the adoption of a cloud computing system in vessel traffic service (VTS) systems and evaluate the relative importance of each factor. Through a literature review and expert Delphi surveys, 12 success factors were derived across the dimensions of technology, organization, environment, and institution. The results of the analytic hierarchy process (AHP) analysis revealed that stability in the technological dimension was the most important factor. This study provides useful implications for future decision-making in VTS cloud adoption by systematically identifying the key success factors and presenting their priorities through the application of the TOE-I framework to VTS cloud computing. Full article

The Automatic Identification System (AIS) utilizes base stations to manage vessel traffic and disseminate waterway information. These stations broadcast maritime safety data to vessels within their service radius using VHF signals. However, the emergence of “spoofing base stations” poses a significant threat to maritime safety. These impostors mimic legitimate AIS base stations by appropriating their Maritime Mobile Service Identity (MMSI) information, interacting with vessels, potentially leading to erroneous decisions, or guiding vessels into hazardous areas. Therefore, ensuring the credibility of AIS base stations is critical for safe vessel navigation. It is essential to distinguish between genuine AIS base stations and “spoofing base stations” to achieve this goal. One criterion for identifying AIS spoofing involves detecting signals beyond the expected service radius of AIS base stations. This paper proposes a method to monitor the credibility of AIS base stations through a service radius detection pattern. Furthermore, the method analyzes the impact of hydrological and meteorological factors on AIS signal propagation in complex sea surface environments. By integrating empirical data, it accurately describes the mathematical relationship and calculates the service radius of AIS base station signals. Analyzing vessel position coordinates, decoding base station position messages, and computing distances between vessels and AIS base stations allows for matching with the AIS base station’s designated service radius and propagation distance. This approach enables precise identification of AIS spoofing base stations, thereby facilitating robust monitoring of AIS base station credibility. The research outcomes provide a foundational framework for developing high-credibility AIS base station services within integrated maritime navigation and information systems. Full article

Maritime transport plays a critical role in global logistics. Compared to road transport, the pace of research and development is much slower for maritime transport. It faces many major challenges, such as busy ports, long journeys, significant accidents, and greenhouse gas emissions. The problems have been exacerbated by recent regional conflicts and increasing international shipping demands. Maritime Autonomous Surface Ships (MASSs) are widely regarded as a promising solution to addressing maritime transport problems with improved safety and efficiency. With advanced sensing and path-planning technologies, MASSs can autonomously understand environments and navigate without human intervention. However, the complex traffic and water conditions and the corner cases are large barriers in the way of MASSs being practically deployed. In this paper, to address the above issues, we investigated the application of Large Language Models (LLMs), which have demonstrated strong generalization abilities. Given the substantial computational demands of LLMs, we propose a framework for LLM-assisted navigation in connected MASSs. In this framework, LLMs are deployed onshore or in remote clouds, to facilitate navigation and provide guidance services for MASSs. Additionally, certain large oceangoing vessels can deploy LLMs locally, to obtain real-time navigation recommendations. To the best of our knowledge, this is the first attempt to apply LLMs to assist with ship navigation. Specifically, MASSs transmit assistance requests to LLMs, which then process these requests and return assistance guidance. A crucial aspect, which has not been investigated in the literature, of this safety-critical LLM-assisted guidance system is the knowledge and safety performance of the LLMs, in regard to ship handling, navigation rules, and skills. To assess LLMs’ knowledge of navigation rules and their qualifications for navigation assistance systems, we designed and conducted navigation theory tests for LLMs, which consisted of more than 1500 multiple-choice questions. These questions were similar to the official theory exams that are used to award the Officer Of the Watch (OOW) certificate based on the Standards of Training, Certification, and Watchkeeping (STCW) for Seafarers. A wide range of LLMs were tested, which included commercial ones from OpenAI and Baidu and an open-source one called ChatGLM, from Tsinghua. Our experimental results indicated that among all the tested LLMs, only GPT-4o passed the tests, with an accuracy of 86%. This suggests that, while the current LLMs possess significant potential in regard to navigation and guidance systems for connected MASSs, further improvements are needed. Full article

An automatic identification system (AIS) is often installed on merchant ships and fishing boats to prevent collisions and ensure safe navigation. The location information of ships transmitted from AIS equipment can help maritime traffic control prevent accidents. The southern coast of Korea comprises a complex coastline with numerous fishing boats and transit vessels. In particular, the Tongyeong and Geoje Islands include high-altitude mountains and islands, resulting in several radio-shaded areas where AIS signals cannot be received, owing to geographical effects. However, only a few studies have explored this region and performed practical experiments on the reception status of AIS locations in radio-shaded areas. In this study, we performed an experiment in the Geoje Island Sea on the southern coast to analyze the impact of high terrain on the reception rate and status of automatic identification devices. Two identical pieces of AIS equipment were installed to generate multiple radio waves, and the location data transmitted via different antennae were compared. The experimental analysis forms the basis for identifying the exact location of ships in the event of maritime accidents, facilitating rapid rescue. Moreover, the accuracy of the location transmitted by the AIS equipment can aid in detecting the cause of accidents. Full article

The International Maritime Organization (IMO) persistently improves policies to mitigate greenhouse gas (GHG) emissions from maritime operations, emphasizing the significance of operational measures. Simultaneously, heightened recognition of collaborative efforts within the maritime sector has increased the applicability of arrival policies like Just-In-Time Arrival (JITA), aimed at curtailing unnecessary anchorage time and emissions affecting adjacent communities in port vicinities. Nevertheless, ongoing initiatives advocate adopting JITA over the prevailing First Come, First Served (FCFS) policy, which is perceived as inefficient and, in the meantime, fair in the shipping industry. This research introduces an integrated decision support model to facilitate the implementation of a sustainable ship queuing policy by the VTS. The model addresses critical concerns, including the priorities of relevant authorities, the duration of nautical services for incoming vessels, and carbon dioxide (CO₂) emissions attributable to anchorage waiting times. The decision support framework presented integrates the Fuzzy Analytical Hierarchy Process (FAHP) and PROMETHEE II methodologies; the study’s outcomes suggest that the model significantly reduces ships’ unnecessary CO₂ emissions during anchorage waiting periods compared to the FCFS policy, with reduction rates ranging from 32.8% to 45% based on case analysis. Moreover, the proposed model ensures fairness by treating competing arriving ships equitably according to predefined criteria. Full article