Automatic Identification System (AIS) data provides crucial information about vessel trajectories. However, raw AIS data is often highly redundant, containing overlapping and repetitive routes, which complicates its direct use in maritime applications such as navigation planning and route prediction. In this paper, we propose an improved simplification algorithm designed to extract typical routes while preserving vessel movement continuity. Our approach simplifies complex AIS data by applying an enhanced distance threshold pruning technique and analyzing the continuity of vessel operations to address route segment discontinuities and coordinate deviations.We conducted experiments to evaluate the impact of the simplification algorithm on deep learning applications, specifically in trajectory prediction and anomaly detection. The results demonstrate that the simplified data significantly improves both training efficiency and prediction accuracy in trajectory forecasting models using deep learning, while also enhancing anomaly detection capabilities. Compared to models trained on the original AIS data, those trained on the simplified data achieved faster convergence and higher precision, with fewer false positives in anomaly detection tasks.The findings highlight the practical advantages of the proposed simplification method, making it a valuable tool for real-time maritime monitoring and improving overall operational efficiency. Our code and data at https://doi.org/10.5281/zenodo.17568672.

With the rapid growth of global maritime trade, the efficient and safe management of maritime traffic has become increasingly critical. This study proposes a comprehensive framework for ship trajectory prediction and maritime traffic congestion identification based on Automatic Identification System (AIS) data. We integrate spatiotemporal analysis with deep learning techniques, specifically combining Graph Convolutional Networks (GCN) and Gated Recurrent Units (GRU) to form a Temporal Graph Convolutional Network (T-GCN) model. This model effectively captures both spatial dependencies among ships and temporal dynamics in traffic flow. Furthermore, we introduce a congestion measurement indicator based on the Speed Performance Index (SPI) to quantify and identify congestion levels in maritime routes. The proposed method not only enhances the accuracy of ship trajectory prediction but also enables proactive congestion warnings, contributing to improved maritime safety and operational efficiency. Experimental results demonstrate the effectiveness of our approach in real-world scenarios.

Spatio-temporal features are crucial for maritime trajectory forecasting, especially in scenarios involving curved waterways or abrupt changes in ship motion patterns. Although Automatic Identification System (AIS) data, which are widely used for trajectory prediction, inherently include temporal and spatial information, effectively strengthening these features and integrating them into prediction models remains challenging. To address this challenge, we propose a Convolutional Neural Network (CNN)-Series-cOre Fused Time Series forecaster (SOFTS)-based framework that explicitly couples spatial and temporal features to achieve high-fidelity maritime trajectory forecasting, especially in scenarios with complex spatial patterns. We first employ a CNN-based spatial encoder to hierarchically abstract spatial density distributions through convolution and pooling operations, thereby learning global spatial structure patterns of ship movements. This encoder emphasizes overall spatial morphology rather than precise individual trajectory points. Second, we employ the SOFTS model to incorporate angular velocity, acceleration, and angular acceleration as input features to characterize ship motion states, which can capture the temporal dependencies of ship motion states from multivariate time series. Finally, the spatial embedding features extracted by the CNN are concatenated with the temporal feature representations learned by SOFTS along the feature dimension to form a joint spatiotemporal representation. This representation is then fed into a fusion regression module composed of fully connected layers to predict future ship trajectories. Experimental results on the validation dataset show that the proposed method achieves an MSE of 0.020 and an MAE of 0.060, outperforming several advanced time series forecasting models in prediction accuracy and computational efficiency. The introduction of angular velocity, acceleration, and angular acceleration features reduces the MSE and MAE by approximately 10.22% and 9.49%, respectively, validating the effectiveness of the introduced dynamic features in improving trajectory prediction performance. These results underscore the proposed method's potential for intelligent navigation and traffic management systems by effectively enhancing inland river navigation safety and strengthening waterborne traffic monitoring capabilities.

The sustainable management of marine resources requires accurate knowledge of fishing activity patterns and their interaction with coastal infrastructure. Intelligent Transportation Systems (ITS) are increasingly applied in the maritime domain, where data-driven approaches enhance safety, efficiency, and sustainability. In this context, Automatic Identification System (AIS) data provide valuable insights into vessel behavior and fisheries management. This study employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify fishing grounds, and a density map-based approach to recognize port locations. By integrating AIS data with machine learning techniques, the study detects and analyzes fishing vessel activities, providing deeper insights into behaviors such as fishing ground visit times, durations, and transitions between fishing grounds and ports. A case study in the Aalesund area of Norway demonstrates that DBSCAN effectively reveals fishing activity patterns relevant to regulatory oversight and spatial planning, while density mapping accurately identifies fishing ports. The findings highlight the potential of AIS-based analytics and clustering methods within maritime ITS frameworks to enhance situational awareness, support compliance with fisheries regulations, and contribute to sustainable marine resource management. Using 2023 AIS data from the Aalesund region, 6 recurrent fishing grounds and 15 port locations are identified, and size-stratified visit frequency and residence-time distributions are quantified together with monthly seasonality in ground usage.

Indonesia’s vast maritime territory faces serious challenges from illegal fishing, smuggling, and habitat destruction. To address these, the Indonesian Navy (TNI-AL) developed Antasena, an artificial intelligence (AI)-powered smart dashboard integrating automatic identification system (AIS) data, satellite imagery, and conservation metrics. Antasena leverages advanced anomaly detection algorithms, achieving 95.3% accuracy, 94.7% precision, 94.2% recall, and a 96.8% receiver operating characteristic-area under the curve (ROC-AUC) score in identifying vessel anomalies, including unauthorized fishing and smuggling activities. Using the analyze, design, develop, implement, and evaluate (ADDIE) framework, the system supports real-time maritime surveillance and biodiversity monitoring in conservation zones. The main contributions of this study include the development of a user-centric AI-based dashboard for maritime anomaly detection, the integration of multi-source data with machine learning models, and validation through operational field tests with maritime authorities. Antasena offers a scalable and effective solution to strengthen maritime security and protect Indonesia’s marine resources.

Seabird responses to altered marine vessel activity during the COVID-19 anthropause: insights from citizen science.

Knowledge about vessel activity in port areas and around major industrial zones provides insights into economic trends, supports decision-making for shipping and port operators, and contributes to maritime safety. Vessel data from terrestrial receivers of the Automatic Identification System (AIS) have become increasingly openly available, and we demonstrate that such data can be used to infer port activities at high resolution and with precision comparable to official statistics. We analyze open-access AIS data from a three-month period in 2024 for Tokyo Bay, located in Japan’s most densely populated urban region. Accounting for uneven data coverage, we reconstruct vessel activity in Tokyo Bay at ~30 m resolution and identify 161 active berths across seven major port areas in the bay. During the analysis period, we find an average of 35±17stat vessels moving within the bay at any given time, and 293±22stat+65syst−10syst vessels entering or leaving the bay daily, with an average gross tonnage of 11,860−50+280. These figures indicate an accelerating long-term trend toward fewer but larger vessels in Tokyo Bay’s commercial traffic. Furthermore, we find that in dense urban environments, radio shadows in vessel AIS data can reveal the precise locations of inherently passive receiver stations.

With the rapid growth of vessel traffic and the widespread adoption of the Automatic Identification System (AIS) in recent years, analyzing maritime traffic flow characteristics has become an essential component of modern maritime supervision. Clustering analysis is one of the primary data-mining approaches used to extract traffic patterns from AIS data. Addressing the challenge of assigning appropriate weights to the multidimensional features in AIS trajectories, namely latitude and longitude, speed over ground (SOG), and course over ground (COG). This study introduces an adaptive parameter optimization mechanism based on evolutionary algorithms. Specifically, Particle Swarm Optimization (PSO), a representative swarm intelligence algorithm, is employed to automatically search for the optimal feature-distance weights and the core parameters of Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling dynamic adjustment of clustering thresholds and global optimization of model performance. By designing a comprehensive clustering evaluation index as the objective function, the proposed method achieves optimal parameter allocation in a multidimensional similarity space, thereby uncovering maritime traffic clusters that may be overlooked when relying on single-dimensional features. The method is validated using AIS trajectory data from the Xiamen Port area, where 15 traffic clusters were successfully identified. Comparative experiments with two other clustering algorithms demonstrate the superior performance of the proposed approach in trajectory pattern analysis, providing valuable reference for maritime regulatory and traffic management applications.

The Bali Strait is one of the busiest sea crossing routes in Indonesia, characterized by high intensity of ship movements and dynamic traffic patterns throughout the day. These conditions require comprehensive analysis to understand the characteristics of vessel movements, identify density zones, and determine peak periods that potentially increase navigation risks. This study aims to analyze ship traffic patterns in the Bali Strait using a combination of K-Means Clustering and Traffic Flow Model based on Automatic Identification System (AIS) data. The dataset consists of 790 AIS records collected during the period of 20–26 June 2025. The research stages include data preprocessing, determination of the optimal number of clusters using the Elbow method, classification of vessel movement behavior using the K-Means algorithm, and analysis of traffic parameters comprising volume, speed, density, and traffic flow. The results reveal the formation of three main clusters: low-speed vessels concentrated around port areas, medium-speed vessels operating on main trajectories, and high-speed vessels dominating the crossing lanes. Evaluation of clustering quality using the Silhouette Coefficient produced a value of 0.3040, indicating a reasonably good level of cluster separation. Furthermore, a consistent peak hour pattern was identified at 12:00, along with two high-density zones located near Ketapang Port and Gilimanuk Port. These findings demonstrate that AIS-based analysis is capable of providing measurable representation of the dynamics of ship traffic in the Bali Strait and has the potential to support operational optimization, enhancement of navigation safety, and consideration for the implementation of a Traffic Separation Scheme (TSS)

This research investigates the development of IT-based Automatic Identification System (AIS) data surveillance models supporting maritime safety through integration of advanced information technology, maritime engineering principles, and human factors optimization. AIS technology generates vast real-time vessel movement data creating unprecedented opportunities for safety enhancement through systematic surveillance, collision risk detection, traffic pattern analysis, and incident prevention, yet effectiveness depends critically on intelligent data processing algorithms, reliable IT infrastructure, and competent personnel capable of interpreting surveillance outputs and taking appropriate actions. Through qualitative analysis involving maritime safety authorities, vessel traffic service (VTS) operators, port authorities, marine engineers, IT specialists, data scientists, and maritime training institutions, this study examines how IT-based surveillance models incorporating pattern recognition, anomaly detection, predictive analytics, and crew-centered interfaces can transform maritime safety management from reactive incident response toward proactive risk prevention. Results demonstrate that intelligent AIS surveillance can identify 75-90% of high-risk situations 15-45 minutes before critical events, reduce collision risks by 60-80%, improve traffic management efficiency by 35-55%, and enhance crew situational awareness by 45-65% when integrated with appropriate training programs developing personnel competencies in data interpretation, system operation, and coordinated response. Key implementation challenges include data quality and completeness issues, computational infrastructure requirements, algorithm development complexity, personnel competency gaps requiring substantial training investments, organizational coordination barriers, and privacy/security concerns. Findings reveal that successful AIS surveillance implementation requires holistic sociotechnical approaches integrating IT systems engineering, maritime domain expertise, and human capability development through coordinated design, deployment, and training strategies. This research contributes to maritime safety literature by providing integrated frameworks for IT-based surveillance systems incorporating technical capabilities, operational requirements, and human factors supporting evidence-based safety management.

Accurate estimation of ship emissions is essential for the effective enforcement of emission control policies in inland waterways. However, existing “bottom-up” models face significant challenges owing to severe data scarcity for inland ships, particularly regarding ship static parameters. This study proposes a novel data fusion and machine learning framework to address this issue. The methodology integrates real-time SO2 and CO2 pollutant concentrations on the Nanjing Dashengguan Yangtze River Bridge, Automatic Identification System (AIS) data, and meteorological information. To address the scarcity of design data for inland ships, web scraping was used to extract basic parameters, which were then used to train five machine learning models. Among them, the XGBoost model demonstrated superior performance in predicting the main engine rated power. A refined activity-based emission model combines these predicted parameters, ship operational profiles, and specific emission factors to calculate real-time emission source strengths. Furthermore, the model was validated against field measurements by comparing the calculated and measured emission source strengths from ships, demonstrating high predictive accuracy with R2 values of 0.980 for SO2 and 0.977 for CO2, and MAPE below 13%. This framework provides a reliable and scalable approach for real-time emission monitoring and supports regulatory enforcement in inland waterways.

ABSTRACT The global crude oil maritime transportation network faces growing risks from shipping route blockages and disruptions at major exporting countries and ports. To address these vulnerabilities, this study constructs a global oil transport network using real-world automatic identification system data and simulates three typical disruption scenarios: chokepoint closures, exporter country shutdowns, and export port failures. Four strategies are proposed to enhance resilience: structural redundancy, node robustness enhancement, optimized recovery scheduling, and cross-modal substitution via pipeline transport. Simulation results show that disruption location notably impacts the resilience of the global crude oil maritime transportation network, with chokepoint failures causing the most severe network performance losses. Each strategy demonstrates scenario-specific strengths: node robustness and optimized recovery perform well under chokepoint and exporter disruptions, structural redundancy is more effective in port-level disruptions, while cross-modal substitution offers valuable flexibility when supply is limited. The integrated strategy consistently delivers the highest resilience improvements, with maximum gains reaching 27.8%. These findings highlight the importance of adopting targeted and adaptive strategies for resilience planning and offer practical implications for resilience enhancement on global maritime oil transport systems.

Global shipping plays a crucial role in international trade. It is responsible for 2-3% of global greenhouse gas emissions. Emissions from maritime transportation on the high seas show considerable variation over time and across different locations. The volume of emissions from ships is closely linked to the level of shipping activity. This study utilizes global shipping Automatic Identification System (AIS) data from 2015 to 2024 to analyze the spatial and temporal patterns and trends of maritime activities in various countries, offering insights and guidance for the targeted development of marine environmental management strategies.

ABSTRACT Accurate prediction of ship trajectories using automatic identification system (AIS) data is critical for intelligent marine traffic management. Although Transformer‐based architectures have improved long‐term prediction performance, their short‐term accuracy remains suboptimal. This study introduces TG‐generative pre‐trained Transformer (GPT), a lightweight trajectory prediction model that embeds a gated recurrent unit (GRU) within a tiny GPT to enhance prediction precision while reducing computational complexity. Using the Danish Maritime Authority AIS dataset containing over 1.2 million trajectory records across multiple vessel types, TG‐GPT was evaluated against state‐of‐the‐art models. For specified routes, TG‐GPT achieves 72%, 85% and 87% reductions in mean absolute error (MAE) at 1, 2, and 3 h prediction horizons, respectively, compared with CLSA. For random trajectory predictions, MAE is reduced by 6%, 5% and 9% relative to TrAISformer. The model further achieves 20% fewer parameters, 10% faster training and 40% faster trajectory generation than Transformer‐based baselines. Unlike previous Transformer‐based models, TG‐GPT introduces a lightweight GRU‐enhanced architecture that significantly improves short‐term prediction accuracy with fewer parameters. These results highlight TG‐GPT's potential as a practical solution for real‐time maritime navigation and autonomous vessel control systems.

Illegal, unreported, and unregulated (IUU) fishing remains a persistent threat in Indonesian waters, causing substantial economic losses and long-term ecological damage. This review synthesizes methods for fusing Automatic Identification System (AIS) data with Synthetic Aperture Radar (SAR) imagery to enhance maritime surveillance. AIS conveys vessel identity and reported position, whereas SAR detects vessels operating without AIS (“dark” vessels). The review covers approaches to spatiotemporal synchronization, data association, and machine-learning models that jointly exploit both modalities. In addition, this study provides a systematic mapping of recent AIS–SAR fusion methods and proposes a conceptual big data framework tailored to Indonesia’s maritime surveillance context. According to the surveyed literature, AIS–SAR fusion has been reported to improve the identification of non-cooperative vessels, reduce false alarm and missed detection rates, and shorten response times. Effective implementation requires reliable spatiotemporal alignment, adequate computing resources for large-scale processing, and interagency data-sharing mechanisms. Collectively, the evidence indicates that large-scale AIS–SAR fusion can enhance maritime awareness and support Indonesia’s efforts to counter IUU fishing.

Validating shipping noise simulations for the Red Sea using field measurements.

Integrating geographic priors and automatic identification system data mining for maritime traffic pattern extraction in complex port waters

Applying an improved object detection algorithm for operational oil spill detection and tracking in synthetic aperture radar images.

This study presents and empirically validates a real-time maritime traffic visualisation system for the Remote Control Centers (RCCs) of Maritime Autonomous Surface Ships (MASS). The proposed framework integrates Automatic Identification System (AIS) preprocessing, static and dynamic information matching, trajectory prediction, and big-data management within a unified Information Convergence Server (ICS). Using real coastal AIS data, the system was quantitatively evaluated for accuracy, responsiveness, and scalability. The static information matching module achieved complete vessel identification, while the dynamic information prediction algorithm maintained trajectory continuity with an average error of about 15 m, even under irregular AIS reporting intervals. The big-data processing pipeline sustained sub-millisecond latency and stable throughput for hundreds of concurrent vessels. The 3D visualisation module provided multi-perspective displays, improving operators’ spatial perception of complex maritime traffic. Empirical results confirm that the system enables accurate real-time fusion and scalable 3D rendering, demonstrating readiness for RCCs operations and Emergency Situations for Remote Control (ESRC). Although the current implementation relies solely on AIS data, its modular design allows future integration of radar, CCTV, and IoT sensor sources. The framework establishes a foundation for next-generation multi-source, data-fusion-based maritime traffic management and future MASS control environments.

Ship positional data are widely used for route inference, yet most existing studies rely on automatic identification system data, which contain irregular transmission intervals and limit the ability to capture vessel-specific operational habits and subtle route choices. This study addresses these limitations by proposing a methodology to infer customary routes using periodic 3 s ship position data collected through the Korean e-Navigation system based on long-term evolution maritime communication. The method comprises three main steps: constructing a sea-area grid with an associated weight map, determining data-driven importance and updating weights, and performing pathfinding. Domestic waters are divided into 100 m grids, and navigable and non-navigable areas are binarized to establish a framework for route exploration. Ship positional data are processed to extract inter-port trajectories, which are then classified by ship size and tidal time zone to account for navigational differences arising from vessel characteristics and tide-dependent accessibility. These trajectories are combined with spatial grids and transformed into a document–word structure, enabling the calculation of movement importance between grid cells using a modified term frequency–inverse document frequency measure. The resulting weights are applied to a pathfinding graph to derive routes that reflect vessel size and tidal conditions. The effectiveness of the proposed method is evaluated by computing cosine similarity between the inferred routes and actual trajectories.