Maritime Traffic Management Research Articles

An adaptive trajectory segmentation and simplification algorithm based on vessel behavioral features

Existing algorithms for trajectory simplification in AIS data processing have issues such as loss of key feature points, high subjectivity in threshold selection, and incomplete consideration of factors. In this paper, an adaptive trajectory segmentation and simplification algorithm based on vessel behavioral features is proposed. The algorithm identifies behavioral feature intervals by extracting vessel behavioral features and constructs an adaptive segmentation threshold function to achieve adaptive segmentation of trajectories and extraction of key feature points. Then, considering the quality, similarity, and spatial variability of trajectory simplification, an adaptive simplification threshold function is constructed to automatically select the best threshold for each sub-trajectory to generate the simplified trajectory. Our experiments demonstrate that the algorithm maintains a high simplification rate while enabling adaptive simplification of the trajectory, and also retains the key feature points and trajectory shapes to the maximum extent. The effectiveness and robustness of the algorithm are verified through comparison with other algorithms, effectively avoiding the distortion of navigational behavior that may be caused by traditional methods. This provides reliable data support for maritime traffic management and decision-making.

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.

Intelligent maritime question-answering and recommendation system based on maritime vessel activity knowledge graph

Traditional maritime traffic management typically relies on positioning data for data mining without incorporating other multi-source data to analyze the maritime vessel activity, which cannot conduct comprehensive maritime knowledge mining. Thus, this study integrates multi-source data, such as trajectory, maritime accident text, and geographic data, to create a maritime vessel activity knowledge graph. On this basis, a question-answering model is developed based on a bidirectional question-answering attention graph neural network, and a personalized recommendation model is developed based on an attention-enhanced joint knowledge propagation and a user preference graph neural network. The former assists users in extracting valuable information from the maritime vessel activity knowledge graph, while the latter predicts the users' potential interests and automatically recommends vessel entities based on their historical query information. Experimental results show that the proposed question-answering model improved the F1-score by 2.31%–10.09% compared to state-of-the-art baseline models on the MVA question-answering dataset. Similarly, the proposed personalized recommendation model improved the click-through rate prediction accuracy by 2.46%–7.05% compared to state-of-the-art baseline models on the MVA personalized recommendation dataset.

Few-shot fine-grained recognition in remote sensing ship images with global and local feature aggregation

Remote sensing ship image detection methods have broad application prospects in areas such as maritime traffic and fisheries management. However, previous detection methods relied heavily on a large amount of accurately annotated training data. When the number of remote sensing ship targets is scarce, the detection performance of previous methods is unsatisfactory. To address this issue, this paper proposes a few-shot detection method based on global and local feature aggregation. Specifically, we introduce global and local feature aggregation. We aggregate query-image global and local features with support features. This encourages the model to learn invariant features under varying global feature conditions which enhances the model’s performance in training and inference. Building upon this, we propose combined feature aggregation, where query features are aggregated with all support features in the same batch, further reducing the confusion of target features caused by the imbalance between base-class samples and novel-class samples, improving the model’s learning effectiveness for novel classes. Additionally, we employ an adversarial autoencoder to reconstruct support features, enhancing the model’s generalization performance. Finally, the model underwent extensive experiments on the publicly available remote sensing ship dataset HRSC-2016. The results indicate that compared to the baseline model, our model achieved new state-of-the-art performance under various dataset settings. This model presented in this paper will provide new insights for few-shot detection work based on meta-learning.

A data mining-then-predict method for proactive maritime traffic management by machine learning

Proactive traffic management is increasingly critical in maritime intelligent transportation systems. Central to this is maritime traffic forecasting, which leverages specific structures and properties of the problem. This study focuses on the traffic dynamics within convergent areas of inland waterways and proposes a method based on data mining followed by prediction using Automatic Identification System (AIS) data. This approach addresses uncertainties in ship voyage destinations and optimizes predictions for temporary stops in inland waterways. AIS data is processed to depict complete ship motion trajectories, grouping them into trajectory sets based on shared origin, destination, and route. These groups help represent maritime traffic patterns using the entrance and exit points of channels and the boundaries of the study area. Additionally, a stop detection model is applied to these trajectories to identify nodes within maritime traffic networks. A decision tree algorithm is then employed to train a classifier for predicting traffic patterns. The method was validated in the convergent area of the Yangtze River and the Hanjiang River, demonstrating effective pattern extraction from inland maritime traffic and high accuracy in predicting single ship trajectories, achieving a 96.7% accuracy rate and 80.9% precision. The findings suggest that the proposed method (1) effectively extracts and predicts traffic patterns, (2) identifies congestion in convergent waters, and (3) supports traffic management strategies.

CSD-YOLO: A Ship Detection Algorithm Based on a Deformable Large Kernel Attention Mechanism

Ship detection and identification play pivotal roles in ensuring navigation safety and facilitating efficient maritime traffic management. Aiming at ship detection in complex environments, which often faces problems such as the dense occlusion of ship targets, low detection accuracy, and variable environmental conditions, in this paper, we propose a ship detection algorithm CSD-YOLO (Context guided block module, Slim-neck, Deformable large kernel attention-You Only Look Once) based on the deformable large kernel attention (D-LKA) mechanism, which was improved based on YOLOv8 to enhance its performance. This approach integrates several innovations to bolster its performance. Initially, the utilization of the Context Guided Block module (CG block) enhanced the c2f module of the backbone network, thereby augmenting the feature extraction capabilities and enabling a more precise capture of the key image information. Subsequently, the introduction of a novel neck architecture and the incorporation of the slim-neck module facilitated more effective feature fusion, thereby enhancing both the accuracy and efficiency of detection. Furthermore, the algorithm incorporates a D-LKA mechanism to dynamically adjust the convolution kernel shape and size, thereby enhancing the model’s adaptability to varying ship target shapes and sizes. To address data scarcity in complex marine environments, the experiments utilized a fused dataset comprising the SeaShips dataset and a proprietary dataset. The experimental results demonstrate that the CSD-YOLO algorithm outperformed the YOLOv8n algorithm across all model evaluation metrics. Specifically, the precision rate (precision) was 91.5%, the recall rate (recall) was 89.5%, and the mean accuracy (mAP) was 91.5%. Compared to the benchmark algorithm, the Recall was improved by 0.7% and the mAP was improved by 0.4%. These results indicate that the CSD-YOLO algorithm can effectively meet the requirements for ship target recognition and tracking in complex marine environments.

An online method for ship trajectory compression using AIS data

Abstract Vessel trajectories from the Automatic Identification System (AIS) play an important role in maritime traffic management, but a drawback is the huge amount of memory occupation which thus results in a low speed of data acquisition in maritime applications due to a large number of scattered data. This paper proposes a novel online vessel trajectory compression method based on the Improved Open Window (IOPW) algorithm. The proposed method compresses vessel trajectory instantly according to vessel coordinates along with a timestamp driven by the AIS data. In particular, we adopt the weighted Euclidean distance (WED), fusing the perpendicular Euclidean distance (PED) and synchronous Euclidean distance (SED) in IOPW to improve the robustness. The realistic AIS-based vessel trajectories are used to illustrate the proposed model by comparing it with five traditional trajectory compression methods. The experimental results reveal that the proposed method could effectively maintain the important trajectory features and significantly reduce the rate of distance loss during the online compression of vessel trajectories.

A Ship Trajectory Prediction Method Based on an Optuna–BILSTM Model

In the field of maritime traffic management, overcoming the challenges of low prediction accuracy and computational inefficiency in ship trajectory prediction is crucial for collision avoidance. This paper presents an advanced solution using a deep bidirectional long- and short-term memory network (BILSTM) and the Optuna hyperparameter automatic optimized framework. Utilizing automatic identification system (AIS) data to analyze ship navigation patterns, the study applies Optuna to fine-tune the hyperparameters of the BILSTM network to improve prediction accuracy and efficiency. The developed Optuna–BILSTM model shows a remarkable 7% increase in prediction accuracy over traditional back propagation (BP) neural networks and standard BILSTM models. These results not only improve ship navigation and safety but also have significant implications for the development of autonomous ship collision avoidance systems, marking a significant step toward safer and more efficient maritime traffic management.

A Novel & Efficient LR LSTM AIS Route Data Prediction for Longer Range

The growth of technology has enabled different industries to generate an excessive amount of data- one such industry being the maritime sector. Sophisticated sensory systems installed on various vessels generate information at a very large scale which can further be used in optimizing operational efficiency, improving safety standards, and aiding in the decision-making process. Researchers have henceforth identified statistical learning methods and machine learning techniques as potent tools for excavating useful insights from this copious amount of data available. This research evaluates how these algorithms work by focusing exclusively on the analysis of sensory data collected from vessels within the maritime domain. A comparison study has been conducted between statistical learning methods (which includes regression analysis, and time series analysis) vis-a-vis machine learning approaches. The major objective of this study was to determine the most effective method for detecting anomalies while simplifying marine operations and optimizing vessel behavior. The scope of the conducted analysis is restricted to the prediction of the next trajectory points. Accurate prediction of vessel positions plays a crucial role in maritime operations, enabling efficient route planning, collision avoidance, and maritime traffic management. In this article, the authors propose a combination model that combines the benefits of Linear Regression (LR) and Long Short-Term Memory (LSTM) techniques to anticipate vessel positions based on Automatic Identification System (AIS) data. The proposed model takes advantage of the interpretability of LR and the temporal dependencies collected by LSTM to capture temporal dependencies, which improve prediction accuracy and reveal the underlying links between vessel features and future positions.

Novel collision risk measurement method for multi-ship encounters via velocity obstacles and temporal proximity

The collision risk assessment of multi-ship encounters in busy waters is of great significance for collision risk early warning and the intelligent supervision of maritime traffic. In this paper, a quantitative and visual evaluation method is proposed to describe changes in multi-ship collision risk in the velocity space according to multi-ship collision risk characteristics. Non-linear velocity obstacles and closest point of approach were combined to measure the risk of ship collision from the perspective of both the difficulty and urgency of the encounter. The proposed method was verified via case studies using historical Automatic Identification System data for the East China Sea. The method advantages were confirmed via comparison to the conventional time-varying collision risk and collision risk index methods. The results show that the proposed method can quantify changes in multi-ship collision risk more intuitively and effectively. The proposed method could be further developed as a new risk indicator for maritime traffic safety management and could aid in the navigation safety of manned and unmanned ships.

A new method of ship detection in complex background of SAR images based on YOLO

Synthetic Aperture Radar (SAR) is a remote sensing technology capable of high-resolution imaging of the ground surface under any weather and light conditions. SAR image ship detection is a technique that uses the ship target features in SAR images to identify and locate ships, which is of great value for applications in the fields of maritime traffic management, marine environment monitoring, and maritime security and defence. However, SAR image ship detection also faces some challenges, such as complex sea surface background, low signal-to-noise ratio, and the diversity and density of ship targets, which lead to the shortcomings of traditional feature extraction and classifier-based detection methods in terms of accuracy and efficiency. To address these challenges, this paper proposes a new method for ship detection in SAR images based on YOLOv7. When dealing with SAR images containing clutter interference and complex backgrounds, many models face the dual challenge of insufficient real-time and accuracy. To address this problem, this study proposes a new PCAN aggregation network, which firstly introduces the SimAM attention mechanism, which significantly enhances the model's ability to identify the differences between ships and background clutter, further improves the ability of focusing on target features, and effectively It further improves the ability to focus on target features, effectively reduces the interference of background noise, and improves the detection accuracy in complex scenes. At the same time, the PConv strategy is added to further lighten the model structure, thus accelerating the computation process and improving the inference efficiency. We trained and tested the model on the SAR-Ship-Dataset dataset, and achieved an accuracy of 91.1%, an F1 score of 92.9, and an FPS of 46, demonstrating its excellent detection capability. By comparing and analysing the model with several classical and cutting-edge methods in the current field, the proposed model in this study demonstrates obvious performance advantages, and in addition, through ablation experiments, we further confirm the effectiveness and contribution of PCAN.

Identification of multi-ship maritime traffic situation based on ship traffic complexity measurement model

Maritime Traffic Situation (MTS) is crucial for enhancing the efficiency and safety of maritime navigation, with the complexity of ship traffic serving as a key aspect of identifying and managing maritime risks. This paper proposes a ship traffic complexity measurement model for MTS. The proposed model quantifies the complexity between ships by evaluating factors such as density, proximity factor, and mitigation index, and then integrates these evaluations into multiple ships interaction scenarios to enable a comprehensive assessment of ship traffic complexity. The results of case study have fully demonstrated the model's capability in identifying potential collision risks during the interaction processes of multiple ships situation within a navigational sea. Research on the ship traffic complexity measurement model can provide strong theoretical and practical support for maritime traffic management and navigational safety.

Night-Time Vessel Detection Based on Enhanced Dense Nested Attention Network

Efficient night-time vessel detection is of significant importance for maritime traffic management, fishery activity monitoring, and environmental protection. With the advancement in object-detection approaches, the method of night-time vessel detection has gradually shifted from traditional threshold segmentation to deep learning that balances efficiency and accuracy. However, the restricted spatial resolution of night-time light (NTL) remote sensing data (e.g., VIIRS/DNB images) results in fewer discernible features and insufficient training performance when detecting vessels that are considered small targets. To address this, we establish an Enhanced Dense Nested-Attention Network (DNA-net) to improve the detection of small vessel targets under low-light conditions. This approach effectively integrates the original VIIRS/DNB, spike median index (SMI), and spike height index (SHI) images to maintain deep-level features and enhance feature extraction. On this basis, we performed vessel detection based on the Enhanced DNA-net using VIIRS/DNB images of the Japan Sea, the South China Sea, and the Java Sea. It is noteworthy that the VIIRS Boat Detection (VBD) observations and the Automatic Identification System (AIS) data were cross-matched as the actual status of the vessels (VBD-AIS). The results show that the proposed Enhanced DNA-net achieves significant improvements in the evaluation metrics (e.g., IOU, Pd, Fa, and MPD) compared to the original DNA-net, achieving performance of 87.81%, 96.72%, 5.42%, and 0.36 Wpx, respectively. Meanwhile, we validated the detection performance of Enhanced DNA-net and strong VBD detection against VBD-AIS, showing that the Enhanced DNA-net achieves 1% better accuracy than strong VBD detection.

YOLOv7-Ship: A Lightweight Algorithm for Ship Object Detection in Complex Marine Environments

Accurate ship object detection ensures navigation safety and effective maritime traffic management. Existing ship target detection models often have the problem of missed detection in complex marine environments, and it is hard to achieve high accuracy and real-time performance simultaneously. To address these issues, this paper proposes a lightweight ship object detection model called YOLOv7-Ship to perform end-to-end ship detection in complex marine environments. At first, we insert the improved “coordinate attention mechanism” (CA-M) in the backbone of the YOLOv7-Tiny model at the appropriate location. Then, the feature extraction capability of the convolution module is enhanced by embedding omnidimensional dynamic convolution (ODconv) into the efficient layer aggregation network (ELAN). Furthermore, content-aware feature reorganization (CARAFE) and SIoU are introduced into the model to improve its convergence speed and detection precision for small targets. Finally, to handle the scarcity of ship data in complex marine environments, we build the ship dataset, which contains 5100 real ship images. Experimental results show that, compared with the baseline YOLOv7-Tiny model, YOLOv7-Ship improves the mean average precision (mAP) by 2.2% on the self-built dataset. The model also has a lightweight feature with a detection speed of 75 frames per second, which can meet the need for real-time detection in complex marine environments to a certain extent, highlighting its advantages for the safety of maritime navigation.

Tying up loose ends together: Cetaceans, maritime traffic and spatial management tools in the Strait of Gibraltar

Abstract The transboundary area of the Strait of Gibraltar is home to seven protected cetacean species that are threatened by high intensity of maritime traffic. More comprehensive knowledge of cetaceans and maritime traffic is required, together with analyses of legislations, strategies and policies. This study quantitatively investigates cetacean distribution and maritime traffic intensity and, for the species of community interest bottlenose dolphin, habitat suitability. Results are qualitatively discussed considering the overlap of cetacean hot spots with different maritime activities and the consistency of spatial conservation management measures in force. The Fixed Line Transect Mediterranean Monitoring Network protocols were followed for 59 visual surveys using ferries as observation platforms for monitoring cetaceans and maritime traffic. Surveys were carried out along the transects between Algeciras and Ceuta and between Algeciras and Tanger‐Med, in 2018 and 2019. 264 cetacean sightings, including seven different species and four near‐miss collision events (involving pilot, sperm and fin whales), were reported. Monitoring cetaceans from ferries in the Strait provided insights into cetacean distribution and maritime traffic, enabling the identification of cetacean hot spots, suitable habitats and maritime traffic high‐risk zones. A transboundary management effort is required, together with an adaptive approach for protecting highly mobile species such as cetaceans. Proposals include a long‐term cetacean monitoring program carried out by dedicated observers on board ferries as a cost‐effective methodology and mandatory training for crew members, to increase cetacean knowledge and reduce collision risk. The designation of an international temporal or, in some zones, permanent speed reduction area (i.e., Cetacean Critical Navigation Zone, with a maximum speed of 13 knots) and of a micro‐sanctuary with a seasonal no‐take zone in the Bay between Algeciras and Gibraltar, together with international surveillance, are recommended measures for the enhancement of conservation efforts in the Strait.

Deep bi-directional information-empowered ship trajectory prediction for maritime autonomous surface ships

It is critical to have accurate ship trajectory prediction for collision avoidance and intelligent traffic management of manned ships and emerging Maritime Autonomous Surface Ships (MASS). Deep learning methods for accurate prediction based on AIS data have emerged as a contemporary maritime transportation research focus. However, concerns about its accuracy and computational efficiency widely exist across both academic and industrial sectors, necessitating the discovery of new solutions. This paper aims to develop a new prediction approach called Deep Bi-Directional Information-Empowered (DBDIE) by utilising integrated multiple networks and an attention mechanism to address the above issues. The new DBDIE model extracts valuable features by fusing the Bi-directional Long Short-Term Memory (Bi-LSTM) and the Bi-directional Gated Recurrent Unit (Bi-GRU) neural networks. Additionally, the weights of the two bi-directional units are optimised using an attention mechanism, and the final prediction results are obtained through a weight self-adjustment mechanism. The effectiveness of the proposed model is verified through comprehensive comparisons with state-of-the-art deep learning methods, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bi-LSTM, Bi-GRU, Sequence to Sequence (Seq2Seq), and Transformer neural networks. The experimental results demonstrate that the new DBDIE model achieves the most satisfactory prediction outcomes than all other classical methods, providing a new solution to improving the accuracy and effectiveness of predicting ship trajectories, which becomes increasingly important in the era of the safe navigation of mixed manned ships and MASS. As a result, the findings can aid the development and implementation of proactive preventive measures to avoid collisions, enhance maritime traffic management efficiency, and ensure maritime safety.

Improving the Maritime Traffic Evaluation with the Course and Speed Model

Recent projections from marine transportation experts highlight an uptick in maritime traffic, attributed to the fourth industrial revolution’s technological strides and global economic rebound. This trend underscores the need for enhanced systems for maritime accident prediction and traffic management. In this study, to analyze the flow of maritime traffic macroscopically, spatiality and continuity reflecting the output of ships are considered. The course–speed (CS) model used in this study involved analyzing COG, ROT, speed, and acceleration, which can be obtained from the ship’s AIS data, and calculating the deviation from the standard plan. In addition, spatiality and continuity were quantitatively analyzed to evaluate the smoothness of maritime traffic flow. A notable finding is that, in the target sea area, the outbound and inbound CS indices are measured at 0.7613 and 0.7501, suggesting that the outbound ship flows are more affected than inbound ship flows to the liquidity of maritime traffic flow. Using the CS model, a detailed quantitative evaluation of the spatiality and continuity of maritime traffic is presented. This approach facilitates robust comparisons over diverse scales and periods. Moreover, the research advances our understanding of factors dictating maritime traffic flow based on ship attributes. The study insights can catalyze the development of a novel index for maritime traffic management, enhancing safety and efficiency.

Adaptive marine traffic behaviour pattern recognition based on multidimensional dynamic time warping and DBSCAN algorithm

Identifying marine traffic behaviour patterns is important for intelligent maritime traffic management systems. The introduction of Automatic Identification System (AIS), which can record speed over ground (SOG) and course over ground (COG) data, makes these patterns accessible. Based on that, a novel adaptive marine traffic behaviour pattern recognition algorithm is proposed to explore potential traffic behaviour patterns. In the proposed recognition algorithm, multidimensional dynamic time warping is introduced to measure the similarity among trajectories; this process considers both the spatial and motion characteristics of trajectories. However, the computational complexity of multidimensional dynamic time warping is high. To reduce the number of required computations, a data simplification algorithm considering ship behaviours is adopted to compress trajectories and, therefore, to achieve an improved computation ability. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to classify marine traffic behaviour patterns. Due to the poor adaptation of the DBSCAN algorithm, we design a local mean density to evaluate the trajectory density distribution in the given dataset and then determine the optimal parameters in DBSCAN according to the local mean density gradient to improve the adaptivity of the algorithm. The proposed approach is illustrated by numerical experiments involving U.S. east coastal waters. The experimental results indicate that our approach is more accurate and adaptive in terms of recognizing hidden traffic behaviour patterns than other methods.

A Semantic Network Method for the Identification of Ship’s Illegal Behaviors Using Knowledge Graphs: A Case Study on Fake Ship License Plates

With the advancement of intelligent shipping, current traffic management systems have become inadequate to meet the requirements of intelligent supervision. In particular, with regard to ship violations, on-site boarding is still necessary for inspection. This paper presents a novel approach for enhancing ships’ management and service capabilities through scientific knowledge graph technology to develop a ship knowledge graph. The proposed approach extracts key characteristics of ship violations from the ship knowledge graph, such as monitoring ships, expired ship certificates, multiple ship tracks, inconsistent ship tracks with port reports, and ships not reported to the port for a long time. Combining the characteristics of ship violations, the approach uses reasoning and identification techniques to detect specific instances of falsely licensed ships and other violations. The development of the ship knowledge graph analysis system enables the identification and verification of illegal ships using fake license plates, while also improving the effective utilization of maritime data and enhancing the ability to make informed decisions related to ship safety. By leveraging cognitive approaches and knowledge graphs, this study offers the potential to develop an intelligent decision-making system for maritime traffic management.

A Benchmark for Maritime Object Detection with Centernet on an Improved Dataset, ABOships-PLUS

Object detection from waterborne imagery is an essential aspect in maritime traffic management, navigation safety and coastal security. Building efficient autonomous systems, which can take decisions in critical situations, requires an integration of complex object detectors in real time. Object detectors trained on generic datasets often give unsatisfactory results in complex scenarios like the maritime environment, since only a fraction of their images contain maritime vessels. Publicly available domain-specific datasets are scarce, and they are limited in the number of images and annotations. Compared to object detection in applications such as autonomous vehicles, maritime vessel detection is considerably reduced in computer vision research. This creates a deficit in exhaustive benchmarking studies for maritime detection datasets. To bridge this gap, we relabel the ABOships dataset and benchmark a state-of-the-art center-based detector, Centernet, on the newly relabeled dataset, ABOships-PLUS. We explore its performance under different feature extractors, and investigate the effect of object size and inter-class variation on detection accuracy. The reported benchmarking illustrates that the ABOships-PLUS dataset is adequate to use in supervised domain adaptation. The experimental results show that Centernet with DLA (Deep Layer Aggregation) as a feature extractor achieved the highest accuracy in detecting maritime objects overall (with mean average precision 74.4%).