AIS data provide a good data source for revealing the distribution of marine fishing activities, assessing marine ecological pressure, and formulating and improving fishery regulation measures. In ...this study, a method of rapidly mapping fishing grounds was proposed, which combined Kernel Density Estimation (KDE) with Hot Spot Analysis (HSA). Then, taking the coastal sea area around the Bohai Strait in China as an example, the fishing ground in the coastal sea area was mapped based on Automatic Identification System (AIS) data, and the effectiveness of fishing regulation measures was analysed. The results showed that the fishing activities in the sea area around the Bohai Strait had obvious monthly and quarterly changes and spatial heterogeneity. Fishery regulation measures such as a summer fishing moratorium, stock enhancement, marine functional zoning, and coastal zones close to motorized bottom trawling have had certain restrictive effects on fishing activities. China has made significant efforts in the restoration of coastal fishery resources and the regulation of fishing activities to promote the sustainable development of fisheries and improve the marine ecological environment.
To guarantee vessel traffic safety in inland waterways, the automatic identification system (AIS) and shore-based cameras have been widely adopted to monitor moving vessels. The AIS data could ...provide the unique maritime mobile service identity (MMSI), position coordinates (i.e., latitude and longitude), course over ground, and speed over ground for the vessels of interest. In contrast, the cameras could directly display the visual appearance of vessels but fail to accurately grasp the vessels’ identity information and motion parameters. In this paper, we propose to improve the maritime traffic surveillance in inland waterways using the robust fusion of AIS and visual data. It is able to obtain more accurate vessel tracking results and kinematic characteristics. In particular, to robustly track the visual vessels under complex scenarios, we first propose an anti-occlusion vessel tracking method based on the simple online and real-time tracking with a deep association metric (DeepSORT) method. We then preprocess and predict the vessel positions to obtain synchronous AIS and visual data. Before the implementation of AIS and visual data fusion, the AIS position coordinates in the geodetic coordinate system will be projected into the image coordinate system via the coordinate transformation. A multi-feature similarity measurement-based Hungarian algorithm is finally proposed to robustly and accurately fuse the AIS and visual data in the image coordinate system. For the sake of repeating fusion experiments, we have also presented a new multi-sensor dataset containing AIS data and shore-based camera imagery. The quantitative and qualitative experiments show that our fusion method is capable of improving the maritime traffic surveillance in inland waterways. It can overcome the vessel occlusion problem and fully utilizes the advantages of multi-source data to promote the maritime surveillance, resulting in enhanced vessel traffic safety and efficiency. In this work, the presented multi-sensor dataset and source code are available at https://github.com/QuJX/AIS-Visual-Fusion.
•A newly-developed multi-sensor dataset related to vessel detection and AIS/visual data fusion.•An anti-occlusion vessel tracking method.•A multi-feature similarity measurement-based AIS and visual data fusion method.•Improvement of maritime traffic surveillance with AIS and visual data fusion under diversified scenes.
With the widespread application of ship Automatic Identification System (AIS) in maritime operations, a large number of ship trajectories become available. This study aims to improve the safety of ...ships navigating through densely trafficked areas and address the challenge of sufficient data exploration while fully describing the traffic conditions in these waters. To achieve this objective, traffic flow information is extracted from AIS data collected in Zhoushan waters. A combination of multi-algorithms is employed to extract the traffic flow frame, specifically, the Douglas-Peucker compression algorithm and trajectory intersection algorithm are utilised to identify the characteristic points of ship trajectories. Subsequently, a density clustering algorithm is applied to extract the three types of characteristic points: compressed trajectory points, intersection points, and ship position points, facilitating data mining efforts. The resulting initial traffic flow characteristic points are then subject to weighted fusion, followed by image superposition processing to create an overlapping map of the ship trajectories. This process culminates in the generation of a traffic flow frame for the region. The framework integrates various track characteristic points, offering insights into the distribution of essential routes in the vicinity waters, thereby providing a comprehensive depiction of ship traffic flow patterns. The proposed framework can be applied to the route planning and serve as a reference to the maritime authorities when selecting recommended shipping lanes.
Recognition and understanding of ship mobility pattern have great significance for intelligent maritime applications, i.e. route discovery and anomaly detection. Besides a number of pattern discovery ...techniques currently derived from ship trajectory, topic modeling popular in the field of Natural Language Processing may provide a novel way to detect implicit patters underlying massive ship trajectories treated as documents. This paper is motivated to apply a semantic analysis method to explore potential mobility patterns from ship trajectories in inland river by combining semantic transformation and topic model. A coarse-grained semantic transformation model is firstly defined to translate each ship trajectory into a document containing a series of sequential motion words. A motion word is generally a synthetic semantic representation of three trajectory features (location, course and speed). All ship trajectories can then be examined and analyzed as a document corpus. A classic topic model (i.e. Latent Dirichlet Allocation, LDA) is employed to explore hidden ship mobility patterns from trajectory document corpus. The effectiveness of this approach is illustrated through a case study at Wuhan waterway, located at middle stream of Yangtze River in China.
•Semantic descriptions of ship location, course over ground and speed over ground has been illustrated.•Raw ship trajectories have been semantically translated into trajectory documents for topic modeling analysis.•Latent Dirichlet Allocation (LDA) model is applied to explore ship motion pattern topics hidden in trajectory documents.•Visualization of topics helps to understand various ship movements and their characteristics.
The ship's routing was adopted to organise marine traffic flow and reduce the risk of collision between ships in crowded waters. With the expansion of the world's fleet, ship traffic in shipping ...bottleneck and chokepoint areas became more and more busy and complex creating serious challenges for navigational safety. Therefore, quantitative collision risk assessment is significantly important for the ships' routeing waters. In this paper, the information entropy method which integrates the K-means clustering based on Automatic Identification System (AIS) data is introduced to quantitatively evaluate the collision risks in the ships' routeing waters. As a case study, the information entropy of Courses Over Ground (COG) for Ningbo-Zhoushan Port (the largest port in the world since 2009) is calculated by using historical AIS data. Then the K-means clustering is used to group the bytes of information entropy of the different legs in the shipping route. We find that in Ningbo-Zhoushan port Precautionary Area (PA) 2, 4 and 7 are the highest risk legs; PA 1, 5 and 6, Traffic Separation Scheme (TSS) 16, and 17 are medium-high risk areas. Therefore, ship collision risk prevention measures should be prioritised in those legs. Our contributions provide a novel approach to quantitatively assess ship collision risks in busy waters.
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.
Coastal nations monitor maritime activities in the interest of defence, security, and safety. This form of monitoring typically occurs at operations centers that visualize the maritime environment by ...creating a Recognized Maritime Picture (RMP) covering a particular area of interest. The creation of this picture changed drastically with the introduction of the Automatic Identification System (AIS). AIS messages are known to contain numerous types of errors and in April 2020 a unique error was found in the data stream. This error consisted of messages indicating the appearance of over 200 vessels in the North Atlantic taking part in a yacht race when in fact no race or physical ships existed. The following work explores the application of various machine learning (ML) techniques to help identify these types of fabricated AIS messages. Specific ML techniques were explored including: K-means clustering, Decision Tree (DT), Random Forest (RF), Feed-Forward Neural Networks (FNN), Support Vector Machines (SVM), and One-Class Support Vector Machines (One-SVM). The results showed that DT, RF, and FNN best identified the fabricated AIS messages with F1 scores greater than 93 percent on the test data.
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 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.
Air pollution from shipping emissions poses significant health and environmental risks, particularly in the coastal regions. For the first time, this region as one of the busiest seas and most ...important international shipping lane in the world with significant nitrogen dioxide (NO2) emissions has been analyzed comprehensively. This paper aims to characterize and quantify the contribution of maritime transport sector emissions to NO2 concentrations in the Red Sea using local Geographically Weighted Regression (GWR) model in a geographic information system (GIS) environment. Maritime traffic volume was estimated using SaudiSat satellite-based Automatic Identification System (S-AIS) data, and the remotely measured tropospheric NO2 concentrations data was acquired from the ozone monitoring instrument (OMI) satellite. A significant spatial variation in the NO2 values was detected across the Red Sea, with values ranging from 4.03 × 1014 to 41.39 × 1014 molecules/cm2. Most notably, the NO2 concentrations in international waters were more than double those in the western coastal regions, whereas the concentrations close to seaports were 100% higher than those over international waters. The results indicated that the local GWR model performed significantly better than the global ordinary least squares (OLS) regression model. The GWR model had a strong and significant overall coefficient of determination with an r2 of 0.94 (p < 0.005) in comparison to the OLS model with an r2 of 0.45 (p < 0.005). Maritime traffic volume and proximity to seaports weighted by shipping activities explained about 94% of the variations of NO2 concentrations in the Red Sea. The results of this study suggest that the S-AIS data and environmental satellite measurements can be used to assess the impacts of NO2 concentrations from shipping emissions. These findings should stimulate further research into using additional covariates to explain the NO2 concentrations in areas near seaports where the standardized residuals are high.
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•GWR model is presented to assess the relations between shipping emissions and NO2.•Local GWR model performed significantly better (94%) than global OLS model (45%).•Over 2.5 million S-AIS messages were used to estimate the ships' activities.•NO2 concentrations were used that were remotely measured by the OMI satellite.•Maritime traffic volume and proximity to seaports explained 94% of NO2 variations.
