Search Results (26)

Low-visibility environments induced by sea fog severely constrain the navigational efficiency and safety in narrow waterways, where traditional radar and Automatic Identification Systems (AIS) frequently encounter challenges such as perception blind spots and information lag. To address this critical issue, this study proposes a UAV-based perception and decision-making methodology for main navigational routes in fog, integrating physical priors with unmanned aerial vehicle (UAV) vision. Firstly, a joint physical dehazing and fog-domain adaptive detection network is constructed. This network addresses the overcomes the interference of non-uniform fog through feature-level enhancement, generating a spatio-temporally continuous visibility field and ship probability grids under a bird’s-eye view (BEV). Subsequently, a quantified “Sailability Score” model is established, providing a scientific basis for the dynamic diversion, speed limitation, and safe distance maintenance of main navigational routes. Simulation-based verifications using real-world fog navigation scenarios in the Qiongzhou Strait, coupled with a joint analysis of Vessel Traffic Service (VTS) and AIS data, suggest that at the critical visibility threshold (≤500 m), the proposed method improves the recall rate of long-distance small target detection by approximately 16.2% and reduces the visibility estimation error by 19.3%. Furthermore, the consistency between the proposed Sailability Score and the actual VTS navigation restriction windows reaches 82.1%, exhibiting a conservative preference for safety (i.e., risk preference ratio $\gamma >1$). Additionally, by introducing a temporal anti-jitter mechanism (parameterized by a smoothing window $\Delta t$), the proposed method extends the navigable time window of the main routes by approximately 12.4% while ensuring navigational safety. The simulation results indicate the framework’s potential perception capabilities and engineering applicability, providing reliable technical support for smart shipping and intelligent VTS systems. Full article

The proactive identification of emerging collision risks is pivotal for maritime traffic safety, particularly in congested hub ports where multi-ship encounters exhibit complex spatiotemporal dependencies. Conventional risk assessment methods, predominantly predicated on instantaneous geometric indicators, often fall short in capturing the systemic evolution of risk. To address these limitations, this study proposes an Improved Spatio-Temporal Graph Convolutional Network (IST-GCN) framework for the short-term forecasting of ship collision risk. The framework models maritime traffic as a rule-integrated dynamic interaction graph, where edge weights are adaptively modulated by navigational rules and the Collision Risk Index (CRI). By leveraging historical observation windows, the model forecasts the maximum collective risk level over a subsequent prediction horizon, categorizing traffic scenes into three ordinal levels: Low, Medium, and High. A comprehensive case study utilizing real-world Automatic Identification System (AIS) data from the core waters of Ningbo–Zhoushan Port demonstrates the efficacy of the proposed approach. The IST-GCN achieves a superior prediction Accuracy of 92.4% and an F1-score of 0.91, significantly outperforming representative baselines including Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and standard ST-GCN. Notably, by explicitly encoding COLREGs-based interaction logic, the framework reduces the False Alarm Rate (FAR) to 8.5% in complex crossing and merging scenarios. These findings indicate that the IST-GCN serves as an interpretable, reliable, and early-warning decision-support tool for intelligent maritime supervision and modern Vessel Traffic Services (VTS). Full article

The Automatic Identification System (AIS) frequently suffers from data loss and irregular report intervals in real maritime environments, compromising the reliability of downstream navigation, monitoring, and trajectory reconstruction tasks. To address these challenges, we propose BAARTR (Boundary-Aware Adaptive Regression for Kinematically Consistent Vessel Trajectory Reconstruction), a novel kinematically consistent interpolation framework. Operating solely on time, latitude, and longitude inputs, BAARTR explicitly enforces boundary velocities derived from raw AIS data. The framework adaptively selects a velocity-estimation strategy based on the AIS reporting gap: central differencing is applied for short intervals, while a hierarchical cubic velocity regression with a quadratic acceleration constraint is employed for long or irregular gaps to iteratively refine endpoint slopes. These boundary slopes are subsequently incorporated into a clamped quartic interpolation at a 1 s resolution, effectively suppressing overshoots and ensuring velocity continuity across segments. We evaluated BAARTR against Linear, Spline, Hermite, Bezier, Piecewise cubic hermite interpolating polynomial (PCHIP) and Modified akima (Makima) methods using real-world AIS data collected from the Mokpo Port channel, Republic of Korea (2023–2024), across three representative vessels. The experimental results demonstrate that BAARTR achieves superior reconstruction accuracy while maintaining strictly linear time complexity ($\mathit{O}\left(\mathit{N}\right)$). BAARTR consistently achieved the lowest median Root Mean Square Error (RMSE) and the narrowest Interquartile Ranges (IQR), producing visibly smoother and more kinematically plausible paths-especially in high-curvature turns where standard geometric interpolations tend to oscillate. Furthermore, sensitivity analysis shows stable performance with a modest training window (n ≈ 16) and minimal regression iterations (m = 2–3). By reducing reliance on large training datasets, BAARTR offers a lightweight, extensible foundation for post-processing in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic Service (VTS), as well as for accident reconstruction and multi-sensor fusion. Full article

Vessel Traffic Service systems play a critical role in maritime safety by providing timely advisories to vessels in congested waterways. However, the optimal timing for VTS operator interventions has remained largely unstudied, relying primarily on subjective operator experience rather than empirical evidence. This study presents the first large-scale empirical analysis of VTS operator intervention timing using automated speech recognition technology applied to actual maritime communication data. VHF radio communications were collected from five major VTS centers in Korea over nine months, comprising 171,175 communication files with a total duration of 334.2 h. The recorded communications were transcribed using the Whisper speech-to-text model and processed through natural language processing techniques to extract encounter situations and advisory distances. A tokenization and keyword framework was developed to handle Maritime English and local-language communications, normalize textual numerical expressions, and facilitate cross-site analysis. Results reveal that VTS operator intervention timing varies by encounter type. In head-on and crossing encounters, advisories are provided at distances, with mean values of 3.1 nm and 2.8 nm, respectively. These quantitative benchmarks provide an empirical foundation for developing standardized VTS operational guidelines and decision support systems, ultimately enhancing maritime safety and operational consistency across jurisdictions. Full article

As the maritime industry transitions toward Maritime Autonomous Surface Ships (MASS), Vessel Traffic Service Operators (VTSOs) face new challenges in managing mixed traffic of conventional and autonomous vessels. Effective VTSO selection is becoming increasingly critical for maritime safety, yet current recruitment processes rely on subjective methods that limit objective evaluation of candidate suitability. This study presents the first machine learning-based classification model for VTSO recruitment. Eight features were defined, including sea service experience, navigation career, education, certifications, and language proficiency. Due to limited access to actual recruitment data, expert-validated simulated datasets were constructed through labeling by 40 maritime professionals and density estimation-based augmentation. Four algorithms were compared, with XGBoost achieving 94.6% F1-score. Feature importance analysis revealed TOEIC score as the most critical predictor, followed by seafaring career, with 3–4 years of experience identified as optimal. These findings indicate that English proficiency for communication with shore remote control centers and practical maritime experience for assessing autonomous vessel behaviors constitute core VTSO competencies in the MASS era. The proposed model demonstrates potential to improve subjective recruitment methods by discovering quantifiable competency patterns, offering a pathway toward data-driven, standardized, and transparent decision-making for enhanced maritime safety. Full article

Detailed planning of vessel traffic on the fairway, carried out by Vessel Traffic Service (VTS) operators, is a complicated task, especially when there are restrictions for two-way ship traffic. Such restrictions take place on the dredged Świnoujście–Szczecin fairway in Poland. After the dredging of the fairway to 12.5 m, vessel traffic regulations introduced in a Port Regulations document have changed, which impacted the course of the decision-making process related to planning vessel traffic on the fairway performed by VTS operators. The aim of the article is to assess the probability of making erroneous decisions related to the admission of non-compliant vessels to traffic on the dredged Świnoujście–Szczecin fairway after the introduction of new vessel traffic regulations. In the article, the tasks carried out by VTS operators during vessel traffic planning were described and analyzed using Failure Mode and Effects Analysis (FMEA) method. The probability of making an erroneous decision at each stage of the planning process was calculated using the Human Error Assessment and Reduction Technique (HEART) method. An event tree was developed in relation to VTS operators’ decision-making on vessel traffic planning performed before and after the introduction of a decision support system (DSS). An expert method was used to determine the probability values. Recommendations were proposed to reduce the risk of making erroneous decisions by VTS operators while vessel traffic planning. The research results contributed to the expansion of knowledge on the impact of new regulation implementation on vessel traffic safety and the risk of making erroneous decisions related to the admission of non-compliant vessels to traffic on the dredged Świnoujście–Szczecin fairway, considering the implementation of a DSS. The results of the study may be of interest to VTS operators, port authorities and maritime administrations. Full article

We propose a complexity-aware extension of the Course–Speed (CS) model that integrates an AIS-derived Traffic Complexity Index (TCI) based on change in speed (ΔV) and course (Δθ) to quantify maneuvering complexity in nonlinear crossing waters. The framework consists of: (i) data preprocessing and gating to ensure navigationally valid AIS samples; (ii) CS index computation using distribution-aware statistics; (iii) TCI estimation from variability in speed and course along intersecting flows; and (iv) an integrated CS–TCI for interpretable mapping and ranking. Using one year of AIS data from a high-density crossing area near the Korean coast, we show that the integrated index reveals crossing hotspots and small-vessel maneuvering burdens that are not captured by spatial regularity metrics alone. The results remain robust across reasonable parameter ranges (e.g., speed filter and σ-based weighting), and they align with operational observations in vessel traffic services (VTS). The proposed CS–TCI offers actionable decision support for port and coastal operations by jointly reflecting traffic smoothness and complexity; it can complement collision-risk screening and efficiency-oriented planning (e.g., energy and emission considerations). The approach is readily transferable to other crossing waterways and can be integrated with real-time monitoring to prioritize control actions in complex marine traffic environments. Full article

Vessel arrival priority determination is a critical factor affecting port safety and efficiency in maritime traffic management, yet existing approaches relying on First Come, First Served (FCFS) principles or empirical judgment have limitations in systematic decision-making. This study aims to develop a systematic decision-making framework that overcomes these limitations by creating an automated, expert knowledge-based priority determination system for vessel traffic services. A dynamic score-based vessel arrival priority determination model was developed integrating the Delphi technique and Fuzzy Analytic Hierarchy Process (Fuzzy AHP). Basic score evaluation factors were derived through Delphi surveys conducted with 50 field experts, and weights were calculated by differentially applying Fuzzy AHP and conventional AHP according to hierarchical complexity. The proposed model consists of a dynamic scoring system integrating basic scores reflecting vessel characteristics and operational conditions, special situation scores considering emergency situations, and risk scores quantifying safety intervals between vessels. To validate the model performance, simulation-based evaluation with eight scenarios was conducted targeting experienced VTS (Vessel Traffic Services) officers, demonstrating strong agreement with expert judgment across diverse operational conditions. The developed algorithm processes real-time maritime traffic data to dynamically calculate priorities, providing port managers and maritime authorities with an automated decision support tool that enhances VTS management and coastal traffic operations. 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

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 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

Global Navigation Satellite System (GNSS) technology supports all phases of maritime navigation and serves as an integral component of the Automatic Identification System (AIS) and, by extension, Vessel Traffic Service (VTS) systems. However, the accuracy of standalone GNSS is often insufficient for specific operations. To address this limitation, various regional and local-area solutions have been developed, such as Differential GNSS (DGNSS), Satellite Based Augmentation Service (SBAS) and Real Time Kinematic (RTK) techniques. A notable development in this field is the recent introduction of the Galileo High-Accuracy Service (HAS), which saw its initial service declared operational by the European Commission (EC) on 24 January 2023. Galileo HAS provides high-accuracy Precise Point Positioning (PPP) corrections (orbits, clocks and signal biases) for Galileo and GPS, enhancing real-time positioning performance at no additional cost to users. This article presents the results of the first Galileo HAS testing campaign conducted at sea using a buoy-laying vessel temporarily equipped with a Galileo HAS User Terminal. The results presented in this Article include accuracy and position availability performance achieved using the Galileo HAS User Terminal. The article also highlights challenges posed by high-power radio-frequency interference, which likely originated from the Long-Range Identification and Tracking (LRIT) system antenna on board the vessel. Furthermore, the article provides additional assessments for different phases of navigation, demonstrating better performance in slow-motion scenarios, particularly relevant to mooring and pilotage applications. In these scenarios, values for horizontal accuracy reached 0.22 m 95% and 0.13 m 68% after removing interference periods. These results are in line with the expectations outlined in the Galileo HAS Service Definition Document (SDD). Full article

Monitoring and counting maritime traffic is important for efficient port operations and comprehensive maritime research. However, conventional systems such as the Automatic Identification System (AIS) and Vessel Traffic Services (VTS) often do not provide comprehensive data, especially for the diverse maritime traffic in Mediterranean ports. The paper proposes a real-time vessel counting system using land-based cameras is proposed for maritime traffic monitoring in ports, such as the Port of Split, Croatia. The system consists of a YOLOv4 Convolutional Neural Network (NN), trained and validated on the new SPSCD dataset, that classifies the vessels into 12 categories. Further, the Kalman tracker with Hungarian Assignment (HA) algorithm is used as a multi-target tracker. A stability assessment is proposed to complement the tracking algorithm to reduce false positives by unwanted objects (non-vessels). The evaluation results show that the system has an average counting accuracy of 97.76% and an average processing speed of 31.78 frames per second, highlighting its speed, robustness, and effectiveness. In addition, the proposed system captured 386% more maritime traffic data than conventional AIS systems, highlighting its immense potential for supporting comprehensive maritime research. Full article

A Vessel Traffic Service (VTS) plays a central role in maritime traffic safety. Regulations are given by the International Maritime Organization (IMO) and Guidelines by the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA). Accordingly, VTS facilities utilize communication and sensor technologies such as an Automatic Identification System (AIS), radar, radio communication and others. Furthermore, VTS operators are motivated to apply Decision Support Tools (DST), since these can reduce workloads and increase safety. A promising type of DST is anomaly detection. This survey presents an overview of state-of-the-art approaches of anomaly detection for the surveillance of maritime traffic. The approaches are characterized in the context of VTS and, thus, most notably, sorted according to utilized communication and sensor technologies, addressed anomaly types and underlying detection techniques. On this basis, current trends as well as open research questions are deduced. Full article