The ship domain concept is of great interest for ship traffic modelling, risk assessment and intelligent collision avoidance. The paper proposes and applies a method to define AIS data-based ...empirical polygonal ship domains, based on traffic density matrices that are derived around each reference ship from an AIS dataset. A modified Quaternion Ship Domain allowing for different shapes for each quadrant is proposed, which results in a better fitting to the empirical domain. The parameters of Quaternion Ship Domains that best fit the empirical polygonal domains are determined for cargo ships and tankers of different lengths. Violations of the Quaternion Ship Domain are then used as an indicator of collision risk that is graphically represented in the study area, providing important information to support maritime traffic monitoring and control tasks.
•Extraction of empirical ship safety domains from AIS data.•Fitting Quaternion Ship Domain to polygonal ship domain.•Quaternion Ship Domain with different quadrant shape parameters.•Collision risk perception related to the number of violations of a ship domain.
A growing concern about the depletion of marine resources due to fishing overexploitation and degradation of ecosystems has been demonstrated over the last decade. Monitoring the spatial and temporal ...distribution of fishing activities is an important tool for fisheries management which can also be used by other sectors such as fisheries science, public authorities, policy-makers and marine spatial planning. In this paper we introduce the first map of fishing activity at a Mediterranean scale of EU and non-EU fishing vessels, extracted using Automatic Identification System ship tracking data. Fishing activity maps were produced for three different years with a spatial resolution of 0.01° × 0.01°. As a main result, for the first time, changes of bottom trawl fishing activities between two consecutive years were map for the whole Mediterranean Sea. The results confirmed the suitability of this monitoring system to obtain reliable information on the extent of bottom trawl fishing activities.
•Marine Spatial Planning needs monitoring the impact of human activities at sea.•AIS can be used as an alternative/integrative source to VMS to map fishing effort.•This manuscript confirms the suitability of AIS to describe the impact on seabed in terms of fishing tracks.•A three-year assessment bottom trawl fishing activity is provided for the whole Mediterranean Sea.•Change in bottom trawl fishing activity between two consecutive years were mapped.
Owing to the rapid economic growth, the importance of the shipping industry has gained prominence. Navigation safety may be prone to hidden dangers if ship collision avoidance measures only depend on ...the decisions of the crew, especially since ship density has increased sharply and ship routes have become more complicated. The real-time path planning of collision avoidance requires more efficient algorithms, the carbon emission constraint is added in the model to limit the sudden speed change of the ship in each trajectory segments, and the path planned by the algorithm is smoother, which can reduce the probability of dangerous accidents. The ship collision avoidance path planning problem with carbon emission constraint is considered in this study, and a nonlinear programming model is established to minimize the mileage and carbon emission in the process of collision avoidance. A modified potential field ant colony algorithm is proposed to solve the model, in which the ant colony algorithm is combined with the modified artificial potential field method for real-time dynamic avoidance. The main idea is to use the potential field to guide the ant colony in the early iterations, and optimize the design of partial components to improve the convergence speed and global optimization of the algorithm. Finally, simulation results show that the modified potential field ant colony algorithm proposed in this study can help improve the accuracy of route prediction and anti-collision, and solve the ship collision avoidance path planning problem effectively based on automatic identification system (AIS) data.
•Improve combination of artificial potential field and ant colony algorithm.•Achieve real-time dynamic collision avoidance of ships.•Limit sudden changes in ship speed to reduce probability of dangerous accidents.•Conduct simulation experiments of three ship encounter situations.
•The ship encounter identification model is formed based on AIS historical data.•A two-stage collision avoidance behavior extraction algorithm is constructed to obtain the collision avoidance ...scheme.•A novel path planning method is established by fusion the collision avoidance trajectory in similar scenarios.
AIS data include ship spatial-temporal and motion parameters which can be used to excavate the deep-seated information. In this article, an interpretable knowledge-based decision support method is established to guide the ship to make collision avoidance decisions with good seamanship and ordinary practice of seamen using AIS data. First, AIS data is preprocessed and trajectory reconstructed to restore the ship historical navigation state, and a ship encounter identification model is constructed according to the encounter characteristics; Second, a two-stage collision avoidance behavior extraction algorithm is formed to build a behavior knowledge base, and the scenario similarity model is constructed to measure and match similar scenarios based on ship position, motion tendency and collision risk. Then, the Delaunay Triangulation Network is used to fuse ship trajectories of similar scenario to form the collision avoidance path. Finally, a case study is performed using the real AIS data outside Ningbo-Zhoushan Port waters, China, and the effectiveness of the planned path is verified by setting the head-on and crossing situations and comparison between the planned and real paths. Results indicate that the proposed model can extract the ship collision avoidance behavior accurately, and the planned path can ensure navigation safety.
Maritime transportation plays an essential role in global trade. Due to the huge number of vessels worldwide, there is also a non-negligible volume of Maritime incidents such as collisions/sinking ...and illegal events (e.g., piracy, smuggling, and unauthorized fishing). Electronic equipment/systems, such as radars and Automatic Identification Systems (AIS), have contributed to improving maritime situational awareness. AIS provides one of the fundamental sources of vessel kinematics and static data. Today, many approaches are focused on automatically detecting the vessels’ traffic behavior and discovering useful patterns and deviations from those data. These studies contribute to detecting suspicious activities and anomalous trajectories, whose developed techniques could be applied in the surveillance systems, helping the authorities to anticipate proper actions. Several concerns and difficulties are involved in the analyses of vessel kinematics data: how to deal with big data generated, inconsistencies, irregular updates, dynamic data, unlabeled data, and evaluation. This article presents the approaches, constraints, and challenges in maritime traffic anomaly detection research, presenting a review, a taxonomy, and a discussion of the proposed approaches.
•Several concerns are involved in the analyses of vessel kinematics data.•Manually detecting anomalous behavior is a tedious and error-prone task.•Anomaly detection is a fundamental task in different domains of knowledge.•In the maritime domain, the nonconforming patterns are referred to as anomalies.
In order to achieve sustainable development of marine fishery resources, effective supervise of trawl fishing during forbidden fishing period is of great significance. This paper addresses the ...challenges of poor generalization and the lack of unstructured information in the precise identification of single trawler fishing behavior. We propose a Transformer network with multi-source information fusion processing (MFGTN), which accurately classifies fishing vessels as single trawl or non-single trawl vessels. Firstly, a private fishing dataset of single trawl behavior is constructed by integrating AIS data with radar data, named HaiNan_SingleTrawlVessel(HN_STV). Subsequently, as fused data lacks unstructured information, it undergoes transformation into trajectory point images and recurrence plot images to reveal the internal structure of the fused data. As such, a visual module is introduced to handle the trajectory point images and recurrence plot images as a branch. Simultaneously, the fused data are input into a Double-Tower Transformer with Dual-gate structures to extract information in different dimensions of the time series and feature space as two separate branches. The Fast Attention module replaces the traditional Attention module to improve network speed and reduce memory consumption. Ultimately, the output of the three branches are fused and controlled by a Dual-gate structure that can autonomously learn to determine the network output. Experimental results show that compared to the current best-performing methods, the method discussed herein on the HN_STV dataset has improved the accuracy, recall, precision, and F1-score performance indicators by 2.34%, 2.46%, 0.97%, and 1.39%, respectively. The AUC area on the ROC curve increased by 4%. In a public dataset including three fishing activities, the proposed method improved accuracy, recall, precision, and F1-score by 2.95%, 2.59%, 2.25%, and 2.70%, respectively, and the AUC area on the ROC curve increased by 3%. And in all experiments, our network incurs the lowest time cost. Therefore, the method proposed herein demonstrates its advanced performance.
•The HN_STV dataset is constructed, which extracts ship trajectories from the surrounding sea area with Hainan Province as the center, and effectively integrates the ship’s AIS data and radar data to create temporal fishing fusion data sequences.•A double-tower Transformer network is proposed to process the information in different dimensions of time series and feature space of temporal fishing fusion data sequences. The traditional Attention module is modified to a Fast Attention module to reduce training time and memory consumption.•A Fusion former visual module is introduced to process the unstructured information of input data, including trajectory point images and recurrence plot images.•A double-layer learnable gate structure is proposed to continuously optimize the weights of different information sources according to the training results of neural networks, so that the network can independently choose the weight ratio of different information sources.
•Based on the encounter evolution, a method is proposed to enable the construction of a ship collision risk model.•A PVO model is developed with an elliptic conflict region, utilizing ddv and tdv to ...quantify state risk of velocities.•A collision risk evolution model is introduced, incorporating spatial attributes, macro-level and micro-level decisions.•Historical scenarios and encounter stages are employed to determine model parameters and their weights.
Aiming at realizing collision risk quantitative evaluation among encounter ships, a novel data-driven evolution model is proposed concerning encounter evolution in maritime transportation. A probabilistic velocity obstacle with an elliptic conflict region is constructed by taking into account uncertainty. The degree of and time to domain violation are introduced to quantify collision risk levels under varying velocities. Then, a ship collision risk evolution model is formulated by considering spatial attributes, macro-level and micro-level evolution based on a realistic collision avoidance decision. The model parameters and their weights are determined by statistical analysis of historical encounter scenarios and the characteristics of encounter stages. Therefore, the model encapsulates the statistical characteristics of actual data, which improves its practical values. The results of case studies indicate that the collision risk evolution model can properly reflect collision risk, so that collision evolution stages can be classified accordingly for rational anti-collision guidance. It brings new contributions to risk visualization, collision avoidance decision-making, and collision accident analysis and responsibility determination.
The maritime Internet of Things (IoT) has recently emerged as a revolutionary communication paradigm where a large number of moving vessels are closely interconnected in intelligent maritime ...networks. However, the tremendous growth of vessel trajectories, collected from the combined satellite-terrestrial AIS (automatic identification system) base stations, could lead to unsatisfactory maritime safety and efficacy. To promote smart traffic services in maritime IoT, it is necessary to accurately and robustly predict the spatiotemporal vessel trajectories. It is beneficial for collision avoidance, maritime surveillance, and abnormal behavior detection, etc. Motivated by the strong learning capacity of deep neural networks, this work proposes an AIS data-driven trajectory prediction framework, whose main component is a long short term memory (LSTM) network. In particular, the vessel traffic conflict situation modeling, generated using the dynamic AIS data and social force concept, is embedded into the LSTM network to guarantee high-accuracy vessel trajectory prediction. In addition, a mixed loss function is reconstructed to make our prediction results more reliable and robust in different navigation environments. Several quantitative and qualitative experiments have been implemented on realistic AIS-based vessel trajectories. Our results have demonstrated that the proposed method could achieve satisfactory prediction performance in terms of accuracy and robustness.
