Ship Behavior Research Articles

Ship Anomalous Behavior Detection Based on BPEF Mining and Text Similarity

Maritime behavior detection is vital for maritime surveillance and management, ensuring safe ship navigation, normal port operations, marine environmental protection, and the prevention of illegal activities on water. Current methods for detecting anomalous vessel behaviors primarily rely on single time series data or feature point analysis, which struggle to capture the relationships between vessel behaviors, limiting anomaly identification accuracy. To address this challenge, we proposed a novel vessel anomaly detection framework, which is called the BPEF-TSD framework. It integrates a ship behavior pattern recognition algorithm, Smith–Waterman, and text similarity measurement methods. Specifically, we first introduced the BPEF mining framework to extract vessel behavior events from AIS data, then generated complete vessel behavior sequence chains through temporal combinations. Simultaneously, we employed the Smith–Waterman algorithm to achieve local alignment between the test vessel and known anomalous vessel behavior sequences. Finally, we evaluated the overall similarity between behavior chains based on the text similarity measure strategy, with vessels exceeding a predefined threshold being flagged as anomalous. The results demonstrate that the BPEF-TSD framework achieves over 90% accuracy in detecting abnormal trajectories in the waters of Xiamen Port, outperforming alternative methods such as LSTM, iForest, and HDBSCAN. This study contributes valuable insights for enhancing maritime safety and advancing intelligent supervision while introducing a novel research perspective on detecting anomalous vessel behavior through maritime big data mining.

Examining the influence of port ship activities on pollutant emissions in port environments.

There is a direct and close relationship between ship emissions in port waters and the operational status of the ships. Precisely identifying the operational status of ships in port waters and thoroughly exploring the specific relationship between these activities and ship emissions is crucial for achieving accurate control and scientific reduction of emissions from ships in port areas. With advancements in technology, AIS data can accurately capture the operational status of ships, facilitating a macro-level analysis of ship behavior and emission characteristics. This study proposes a method for extracting ship activity states using AIS data and conducts a detailed examination of ship activities at various container terminals and berths within Shanghai Port. Additionally, it analyzes the correlation between ship activities and their emissions. The research findings indicate that container ships are the most prevalent ships at Shanghai Port, followed by bulk carriers, general cargo ships, and tankers. Notably, bulk carriers and tankers exhibit the longest berthing times, with tankers showing the most significant fluctuations. Mingdong Terminal is identified as the most active, featuring the longest anchorage and berthing time, whereas Jungonglu and Zhanghuabang Terminals have the shortest. The calculation of pollutant emissions reveals that container ships and oil tankers are the primary contributors, particularly for CO2 and NOx emissions. Mingdong Terminal recorded the highest total CO2 emissions, followed by Guandong and Shengdong Terminals. These findings emphasize the importance of berth service efficiency, suggesting that increased emissions are associated with heightened ship activity. CO2 emissions from container ships increase linearly with the number of berthed ships and show a cubic nonlinear relationship with berthing time. The analysis highlights terminals such as Guandong, Zhendong, Shengdong, and Yangshan Phase IV as critical areas for emission reduction strategies. This study not only enhances our understanding of the dynamics of ship emissions in port waters but also provides a theoretical foundation for implementing effective emission control and reduction strategies. Through this detailed research approach, it is possible to more accurately identify emission sources and optimize port operations, thereby offering strong data support and scientific guidance for environmental management and policy-making in port waters.

A comprehensive framework incorporating deep learning for analyzing fishing vessel activity using automatic identification system data

Abstract Automatic Identification System (AIS) has emerged as a crucial and cost-effective tool for monitoring ship behavior, widely employed in various fisheries. However, extracting meaningful insights from extensive AIS data to support fishery research remains challenging. In this study, we developed a framework integrating deep learning for marine fishing activity analysis, leveraging AIS data alongside marine environmental factors. Our approach utilized a transformer-based model with a majority vote for classifying fishing vessel types. The model achieved high accuracy, surpassing 90% in vessel type classification using a small subset of AIS records. Our framework employed the Temporal K-Means algorithm to efficiently identify fishing behavior, leveraging the time-series information of AIS data. Subsequently, it mapped fishing hours onto spatial grids to analyze the relationship between fishing activity and environmental factors. Correlation analysis revealed distinct preferences of different vessel types for environmental conditions, influencing their spatial distributions. Trawlers, for instance, exhibited sensitivity to seafloor bottom temperature, whereas seiners were primarily influenced by sea surface density (SSD) and sea surface temperature and gillnetters by SSD. Through this framework, we have established a coherent process to derive valuable insights about fishery resources from AIS data and guide fisheries management.

A machine learning method for the recognition of ship behavior using AIS data

The efficiency of maritime traffic management and the safety of ship navigation have become top priorities. This study introduces a ship behavior recognition method that utilizes the Extreme Gradient Boosting (XGBoost) classification model, in conjunction with the Sparrow Search Algorithm (SSA), to enhance proactive maritime traffic management. The method leverages Automatic Identification System (AIS) data as its primary source and involves several critical steps. Initially, the AIS data is preprocessed, and ship behaviors are encoded. Subsequently, the encoded behaviors are clustered using spectral clustering to create a labeled dataset. Then, this dataset is employed to train and validate the SSA-XGBoost classification algorithm for identifying ship behaviors. Finally, an example analysis is performed in the Yangtze River. The results indicate that the proposed method can accurately and swiftly identify ship behaviors, achieving an accuracy of 97.28%, precision of 96.97%, recall of 97.43%, and an F1 score of 97.19%, surpassing the performance of the existing algorithms. The findings have the potential to aid maritime supervision authorities in promptly assessing ship navigation statuses and provide a valuable reference for developing ship scheduling decisions.

An Analysis and Comparison of the Hydrodynamic Behavior of Ships Using Mesh-Based and Meshless Computational Fluid Dynamics Simulations

This paper presents a comparison of two turbulence models implemented in two different frameworks (Eulerian and Lagrangian) in order to simulate the motion in calm water of a displacement hull. The hydrodynamic resistance is calculated using two open-source Computational Fluid Dynamics (CFD) software packages: OpenFOAM and DualSPHysics. These two packages are employed with two different numerical treatments to introduce turbulence closure effects. The methodology includes rigorous validation using a Wigley hull with experimental data taken from the literature. Then, the validated frameworks are applied to model a ship hull with a 30 m length overall (LOA), and their results discussed, outlining the advantages and disadvantages of the two turbulence treatments. In conclusion, the resistance calculated with OpenFOAM offers the best compactness of results and a shorter simulation time, whereas DualSPHysics can better capture the free-surface deformations, preserving similar accuracy.

Hydroelasticity of a 21000TEU containership under freak waves by fluid-flexible structure interaction simulations

In this paper the CFD-FEM two-way coupled method is used to simulate the motions and wave loads on a 21000TEU containership subjected to freak waves. A numerical wave tank is established for the generation of long-crested freak waves by wave focusing method based on superimposing sinusoidal component waves. The ship hull is modeled with a backbone beam which reproduces the vertical bending vibration modal behaviour of real ship. The simulated freak waves are checked and the evolution of freak waves during propagation is analyzed first. The ship responses in regular waves at different wave heights and ship speeds are compared. The extreme responses of ship motion and sectional loads in freak waves are analyzed and compared with those in regular waves. The influence of ship forward speed and wave height of freak waves on ship motion and load responses are analyzed and discussed.

A Hierarchical Pricing Strategy for Shore-to-Ship Power Services Considering Ship Behaviors

A Hierarchical Pricing Strategy for Shore-to-Ship Power Services Considering Ship Behaviors

A Novel WTG Method for Predicting Ship Trajectories in the Fujian Inshore Area Based on AIS Data

The increasing congestion in major global maritime routes poses significant threats to international maritime safety, exacerbated by the proliferation of large, high-speed vessels. To improve the detection of abnormal ship behavior, this research employed automatic identification system (AIS) data for ship trajectory forecasting. Traditional methods primarily focus on spatial and temporal correlations but often lack accuracy and reliability. In this study, ship path predictions were enhanced using the WTG model, which combines wavelet transform, temporal convolutional networks (TCN), and gated recurrent units (GRU). Initially, wavelet decomposition was applied to deconstruct the input trajectory time series. The TCN and GRU modules then extracted features from both the time series and the decomposed data. The predicted elements were reassembled using a multi-head attention mechanism and a pooling layer to produce the final predictions. Comparative experiments demonstrated that the WTG model surpasses other models in the accuracy of ship trajectory prediction. The model proposed in this study proves to be reliable for forecasting ship paths, which is crucial for marine traffic management and ensuring safe navigation.

Research on Anomaly Detection of Ship Behavior Based on Data Mining

With the increase in the number of ships and the intensifying maritime traffic, maritime accidents such as ship collisions and grounding occur frequently, posing serious threats to people's lives and property. Therefore, timely detection and correction of abnormal ship behaviors are of great significance for improving navigation safety. The Automatic Identification System (AIS) can provide real-time information on ships' positions, speeds, headings, and more, offering a rich data resource for data mining. Through the analysis of these data, it is possible to deeply explore the navigation patterns and characteristics of abnormal behaviors of ships. This paper applies data mining technology to the detection of abnormal ship behaviors, which not only improves the accuracy and efficiency of detection but also provides maritime regulatory authorities with richer decision-making support.

Numerical analysis of coupling effect between sloshing and motion responses of a KCS in uni- and bi-directional waves

The seakeeping behaviour can be severely affected by the coupling between sloshing and ship motions in waves. The sloshing behaviour of liquid-loaded ships in bi-directional waves differs significantly from that in uni-directional waves. Given that ships operate in short-crested or multi-directional waves during their whole service period, it is significant to gain a deeper understanding of the effect of wave directionality on the coupling between sloshing and ship motions. In this study, a KRISO container ship (KCS) model with type-B liquefied natural gas (LNG) cargo tanks is adopted for coupling analysis. The 2D long-crested regular wave is adopted as an uni-directional wave, and three dimensional (3-D) cross wave is adopted as a bi-directional wave. The computational fluid dynamics (CFD) method is applied for the sloshing and seakeeping simulation of the ship model in different wave fields. The differences in wave elevation, sloshing forces, and coupling effects between ship motions and sloshing are studied in these two wave fields by adjusting control parameters such as ship heading angle and filling ratio. The comparative results indicate that sloshing behaviour and coupled motion responses in cross waves should be paid enough attention during ship design and evaluation.

AIS Data Driven Ship Behavior Modeling in Fairways: A Random Forest Based Approach

The continuous growth of global trade and maritime transport has significantly heightened the challenges of managing ship traffic in port waters, particularly within fairways. Effective traffic management in these channels is crucial not only for ensuring navigational safety but also for optimizing port efficiency. A deep understanding of ship behavior within fairways is essential for effective traffic management. This paper applies machine learning techniques, including Decision Tree, Random Forest, and Gradient Boosting Regression, to model and analyze the behavior of various types of ships at specific moments within fairways. The study focuses on predicting four key behavioral parameters: latitude, longitude, speed, and heading. The experimental results reveal that the Random Forest model achieves adjusted R2 scores of 0.9999 for both longitude and latitude, 0.9957 for speed, and 0.9727 for heading. All three models perform well in accurately predicting ship positions at different times, with the Random Forest model particularly excelling in speed and heading predictions. It effectively captures the behavior of ships within fairways and provides accurate predictions for different types and sizes of vessels, especially in terms of speed and heading variations as they approach or leave berths. This model offers valuable support for predicting ship behavior, enhancing ship traffic management, optimizing port scheduling, and detecting anomalies.

Some Observations on the Strength of Viking Ship Rudders

Technical papers which attempt to analyse historic and ancient ship structures using modern techniques appear only spasmodically in today’s learned engineering journals. There could be many good reasons for this; the lack of commercial incentives, the paucity of accurate engineering data and perhaps a certain reluctance to cross into an area which is rightly the province of the nautical archaeologist. This, in the author’s view is a pity since simplified analytical techniques of the type routinely used in design offices can offer useful cost-effective insights into the physical behaviour of historic ships and craft, without endangering the physical reconstruction or its crew. In addition as the input data are often only loosely defined, less sophisticated methods are ideal for conducting “data-sensitivity” investigations. One such paper (Loscombe, 2022) described efforts to apply modern local and global structural design methods to Viking-age ships with a view to establishing plausible operational factors of safety for comparison with modern requirements. This follow-up contribution continues with the theme by focusing on the rudder. Rudder failure can endanger any ship, ancient, medieval or modern but the Viking ship rudder has a number of structural features not found on modern vessels which invite retrospective stress analyses. One component in particular, the lower bearing in modern terminology, appears to have a very short operational life, measured in days rather than years as is the expectation for today’s marine vehicles and hence the Viking ship rudder is a good candidate for such simplified numerical analyses.

Development and Application of an Advanced Automatic Identification System (AIS)-Based Ship Trajectory Extraction Framework for Maritime Traffic Analysis

This study addresses the challenges of maritime traffic management in the western waters of Taiwan, a region characterized by substantial commercial shipping activity and ongoing environmental development. Using 2023 Automatic Identification System (AIS) data, this study develops a robust feature extraction framework involving data cleaning, anomaly trajectory point detection, trajectory compression, and advanced processing techniques. Dynamic Time Warping (DTW) and the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithms are applied to cluster the trajectory data, revealing 16 distinct maritime traffic patterns, key navigation routes, and intersections. The findings provide fresh perspectives on analyzing maritime traffic, identifying high-risk areas, and informing safety and spatial planning. In practical applications, the results help navigators optimize route planning, improve resource allocation for maritime authorities, and inform the development of infrastructure and navigational aids. Furthermore, these outcomes are essential for detecting abnormal ship behavior, and they highlight the potential of route extraction in maritime surveillance.

Online Ornstein–Uhlenbeck based anomaly detection and behavior classification using AIS data in maritime

Detecting anomalies in maritime systems involves recognizing vessel patterns that are not in line with what is expected or planned prior to the voyage. These patterns are identified for a variety of reasons at different stages. However, the available approaches are mostly focused on batch processing, which assumes that all data are available at once. Furthermore, limited attention has been paid to the identification of vessel behavior in the maritime domain. To gain a deeper understanding of the behavior of the vessel, this paper proposes a framework for online vessel anomaly detection and behavior identification for streaming AIS data. The framework employs an Ornstein–Uhlenbeck (OU)-based algorithm for anomaly detection and a machine learning-based behavior identification algorithm for ship behavior classification. In particular, we use the XGBoost algorithm as a classification method to distinguish various behaviors of vessels within the maritime domain. The proposed framework does not require labeled data for anomaly detection, and shows robustness in the presence of missing information (a passage plan, which contains the parameters to be followed during the voyage). The numerical results demonstrate that the proposed approach can achieve reasonable values of the F1 score for the various activities of the vessels.

An efficient Meta-VSW method for ship behaviors recognition and application

Ship behaviors refer to the operational process such as sailing, entering into port/departure, etc., which indicate by their position, speed, and so on. The collected big data normally have been treated by unsupervised Machine Learning methods. However, the process is time consuming and lacks consideration of time continuity. From the unknown data to recognize and recur the ship behaviors is still a complex problem. Hence, this study proposes a universal Meta-trajectory Variable Sliding Window (Meta-VSW) method to provide an efficient and high-fidelity solution. In this method, the ship data were connected into the smallest units by the meta-trajectory coding, and combines with variable sliding windows to achieve fast, continuous and accurate recognition of ship behaviors. Taking an inland-water ship and a marine transport ship as examples, the validity of the method was fulfilled and compared with two commonly used algorithms, Affinity Propagation (AP) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It has the fastest computational speed and can effectively classify the behaviors of massive unknown data from different ships. And it has good performance in capturing behavior boundaries, with the recognition accuracy up to 0.9. Then, the method was applied to analyze the operational effectively and fuel consumption.

A Deep Generative Model for Multi-Ship Trajectory Forecasting With Interaction Modeling

Abstract Multi-agent modeling is a challenging issue in intelligent systems, which is further compounded by heavy and complex traffic in maritime contexts. Trajectory forecasting can enhance operation safety. Nonetheless, effectively modeling interactions among vessels poses a significant difficulty. Toward this end, we propose a conditional variational autoencoder approach to ship trajectory prediction in a dynamic and multi-modal encounter situation. Leveraging a shared recurrent neural network architecture and attention mechanism, our method aggregates vessel trajectory data, enabling the model to learn and encapsulate meaningful encounter information across active vessels. We utilize automatic identification system data from the Oslofjord region to validate our approach. Through comprehensive experiments conducted on a four-ship encounter dataset, our proposed model demonstrates promising performance, by outperforming the benchmark models. Furthermore, we analyze the prediction model in a wide array of dimensions, showcasing its proficiency in complex ship behaviors learning, modeling ship interaction, and approximating actual trajectories.

First-principles algorithms for ship motion simulation based on ARMA and trochoidal wave models

An approach for the direct simulation of ship behavior in heavy sea conditions using modern computers methods is described. The approach includes a model for wind-driven waves, algorithms for hydrodynamic pressure calculation, a solution of the diffraction problem, and the integration of the approach into a problem solution environment. An ARMA (autoregressive-moving average) model is used for the simulation of a hydrodynamically adequate spatial-temporal wave surface and integrated with a calculation of hydrodynamic pressure at any point under the wave surface. The integration of the wave pressure over the ship hull and the solution of the diffraction problem provides the excitation forces and moments and permits direct numerical simulation of ship motions in waves. An effective numerical implementation of the algorithms with mapping to modern computer architecture is described. The combination of the numerical algorithms with a visual simulation environment is presented as a virtual testbed.

Continuous mapping of deformation over a ship structure model based on pointwise measurements

The development of structural monitoring systems on-board vehicles aims to achieve a full-picture of the vehicle response under operating conditions. The use of different data sources is a key point for many SHM approaches which demand for extensive information and data fusion techniques to address the specific inverse problem in a robust way. Therefore, a procedure to transform pointwise measurements into a continuous representation of the monitored structure is presented in this paper. To achieve the desired monitoring goals, a numerical approach which combines experimental recorded data and modelled responses from a ship structural model is developed. Craig-Bampton truncation technique is applied on the FE vehicle model to reduce elastic dofs and obtain a computationally cost-effective model. The main advantage of Craig-Bampton modes is the capability to keep unchanged the information at the sensor nodes. A state observer is then built to evaluate the elastic deflection field and the loads over the structure in the points where no measurement is available. A natural second-order observer is exploited as it is more suited for the present application than the Kalman filter. To enhance the observer performances, an optimization process is carried out based on a learning phase. Following a previous paper about theoretical aspects of the virtual sensing approach on 1D structures. the developed techniques are here applied to the monitoring of the structural deflections and the ambient excitation relative to an elastically scaled model of a fast catamaran, tested in the towing-tank. Accelerations and strain-gage data are used to reconstruct the elastic deformations of the physical model metallic backbone scaling the reference ship behavior. Hydrodynamic load data relative to the demihull portions and the wetdeck are obtained as well.

Ship Trajectory Classification Prediction at Waterway Confluences: An Improved KNN Approach

This study presents a method to support ship trajectory prediction at waterway confluences using historical Automatic Identification System (AIS) data. The method is meant to improve the recognition accuracy of ship behavior trajectory, assist in the proactive avoidance of collisions, and clarify ship collision responsibility, to ensure the safety of waterway transportation systems in the event of ship encounters induced by waterway confluence or channel limitation. In this study, the ship trajectory based on AIS data is considered from five aspects: time, location, heading, speed, and trajectory by using the piecewise cubic Hermite interpolation method and then quickly clustered by regional navigation rules. Then, an improved K-Nearest Neighbor Algorithm considering the sensitivity of data characteristics (SKNN) is proposed to predict the trajectory of ships, which considers the influence weights of various parameters on ship trajectory prediction. The method is trained and verified using the AIS data of the Yangtze River and Han River intersection in Wuhan. The results show that the accuracy of SKNN is better than that of conventional KNN and Naive Bayes (NB) in the same test case. The accuracy of the ship trajectory prediction method is above 99% and the performance metrics of the SKNN surpass those of both the conventional KNN and NB classifiers, which is helpful for early warning of collision encounters to ensure avoidance.

Parametric study of side collision-induced denting failures on the ultimate strength of a handy-size containership under vertical bending

This study focuses on analysing the collision behaviour and residual ultimate bending strength of a container ship following a collision. Initially, computational models employing nonlinear finite element analysis (NFEA) were used to consider dynamic material parameters like strain rate and crack strain. The accuracy of this numerical approach was verified by comparison with test results. Subsequently, various collision scenarios were analyzed using ABAQUS software, encompassing diverse impact velocities, indenter shapes, and boundary conditions. The aim was to evaluate the reduction in the residual ultimate bending strength of a container ship hull resulting from collisions at both sagging and hogging moments. Finally, empirical equations were formulated to forecast the residual ultimate bending strength based on numerical findings. These equations exhibited excellent accuracy when validated against numerical and experimental data. This methodology and its equations provide valuable tools for assessing the post-collision strength of container ships and enhancing maritime transportation safety.