Automatic Identification System Data Research Articles

Enhancing vessel arrival time prediction: A fusion-based deep learning approach

The logistic community of shippers has struggled to predict the precise arrival time of the seagoing vessels with reliable certainty. While deep-learning approaches are promising, the existing methods fail to provide desirable results due to a shallow prediction architecture. This research work proposes a method to predict vessel arrival time that could eventually be incorporated into an intelligent decision support system that we call Vessel Arrival Time Prediction (VATP). VATP presents a hybrid architecture of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Attention mechanism, Dropout, and Dense layers to take advantage of the coarse-grained local features produced by CNN, the longitudinal nature of AIS (long-time dependencies) via LSTM and paying attention to the influence feature on the arrival time. While many prior approaches have relied solely on AIS data, and some incorporated a combination of AIS and vessel information, our method also integrates diverse data sources, including Automatic Identification System (AIS), Augmented information (Ain), and Maritime Weather Data (MWD). Furthermore, only a few methods have considered weather information, often using minimal weather-related features. In contrast, our approach involves a comprehensive range of weather-related features, and besides these data sources, we extracted specially crafted features.To verify the effectiveness of VATP, we conducted our experiment on large-scale datasets. The VATP model obtained a Root Mean Square Error (RMSE) of 10.63 and a Mean Absolute Percentage Error (MAPE) of 35.11 %. Our results demonstrate that VATP achieves significant performance. Furthermore, these positive results demonstrate that i) the accuracy of the VATP approach can be improved using the AIS, MWD, and Ain information; ii) learning from a unified feature set can result in a significant performance improvement compared to learning from a subset of the features; iii) we also obtained a superior performance in comparison with other well-known methods in the literature and various state-of-the-art baseline methods. Finally, our results illustrate the consistent performance of the VATP across different datasets.

Underwater sound speed profile estimation from vessel traffic recordings and multi-view neural networks.

Sound speed is a critical parameter in ocean acoustic studies, as it determines the propagation and interpretation of recorded sounds. The potential for exploiting oceanic vessel noise as a sound source of opportunity to estimate ocean sound speed profile is investigated. A deep learning-based inversion scheme, relying upon the underwater radiated noise of moving vessels measured by a single hydrophone, is proposed. The dataset used for this study consists of Automatic Identification System data and acoustic recordings of maritime vessels transiting through the Santa Barbara Channel between January 2015 and December 2017. The acoustic recordings and vessel descriptors are used as predictors for regressing sound speed for each meter in the top 200 m of the water column, where sound speeds are most variable. Multiple (typically ranging between 4 and 10) transits were recorded each day; therefore, this dataset provides an opportunity to investigate whether multiple acoustic observations can be leveraged together to improve inversion estimates. The proposed single-transit and multi-transit models resulted in depth-averaged root-mean-square errors of 1.79 and 1.55 m/s, respectively, compared to the seasonal average predictions of 2.80 m/s.

Procjena moguće pogrešne identifi kacije AIS transpondera unutar AIS podataka zbog inverzije MMSI bitova

For almost two decades now, the automatic identification system (AIS) has contributed to increasing the safety of navigation at sea. In addition to that, the system can provide valuable data for surveillance of maritime traffic on a global scale. For reliable tracking the movement of vessels with AIS, it is important to correctly identify the craft. Nowadays, such identification is based entirely on the maritime mobile service identity (MMSI). Despite extensive research being done on the subject of anomalies detectable within the AIS, with emphasis on spotting and correcting erratic vessel movements, a possibility of accidental falsification of MMSI leading to incorrect track reconstruction of a vessel has so far been neglected. This study focuses on analysing hypothetical situations when the MMSI of an AIS transponder on board a vessel is altered within the transponder software and a valid AIS data message is sent with a false MMSI which accidentally belongs to a completely different vessel. Since such MMSI transformation leaves no traces, an assessment of a speed-based plausibility is additionally proposed to figure out how many MMSI based injections of false AIS track points can be detected.

Graph deep learning recognition of port ship behavior patterns from a network approach

The detection and recognition of ship mobile behavior patterns based on automatic identification system (AIS) data are crucial for ship navigation and maritime supervision. However, present methods, such as rule-based constraints and density clustering extraction, still suffer from restrictions imposed by artificial settings of parameters and thresholds. The lack of neighborhood context information in unstructured and serialized ship trajectory vector data makes it challenging to incorporate advanced methods such as deep learning for pattern learning. This study builds multiple semi-supervised deep learning graph neural network (GNN) models based on the message passing paradigm for recognizing ship mobile behavior patterns by constructing an AIS trajectory network and computing multi-order node features through Delaunay triangulation. It can get rid of the dependence on parameters of traditional rule-based and unsupervised learning methods. It also reduces the dependence on auxiliary information in trajectory qualitative by introducing multi-order node features. We conducted the experiments on cargo ship data from New York and Singapore to test our method. The model's classification accuracy is more than 99% for the New York dataset and more than 95% for the Singapore dataset. This method can be extended to other types of ships and traffic trajectory vector data with the excellent application potential of GNN in pattern recognition of vector mobile data. The code and experimental data are available at: https://github.com/destiny1103/DT-GNN.

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.

Identification and Positioning of Abnormal Maritime Targets Based on AIS and Remote-Sensing Image Fusion.

The identification of maritime targets plays a critical role in ensuring maritime safety and safeguarding against potential threats. While satellite remote-sensing imagery serves as the primary data source for monitoring maritime targets, it only provides positional and morphological characteristics without detailed identity information, presenting limitations as a sole data source. To address this issue, this paper proposes a method for enhancing maritime target identification and positioning accuracy through the fusion of Automatic Identification System (AIS) data and satellite remote-sensing imagery. The AIS utilizes radio communication to acquire multidimensional feature information describing targets, serving as an auxiliary data source to complement the limitations of image data and achieve maritime target identification. Additionally, the positional information provided by the AIS can serve as maritime control points to correct positioning errors and enhance accuracy. By utilizing data from the Jilin-1 Spectral-01 satellite imagery with a resolution of 5 m and AIS data, the feasibility of the proposed method is validated through experiments. Following preprocessing, maritime target fusion is achieved using a point-set matching algorithm based on positional features and a fuzzy comprehensive decision method incorporating attribute features. Subsequently, the successful fusion of target points is utilized for positioning error correction. Experimental results demonstrate a significant improvement in maritime target positioning accuracy compared to raw data, with over a 70% reduction in root mean square error and positioning errors controlled within 4 pixels, providing relatively accurate target positions that essentially meet practical requirements.

Fishing effort dynamics around the Galápagos Marine Reserve as depicted by AIS data.

The waters around the Galápagos Marine Reserve (GMR) are important fishing grounds for authorized artisanal vessels fishing within the reserve as well as for national and foreign industrial fleets operating in the wider Ecuadorian Insular Exclusive Economic Zone (IEEZ). Although it was not originally designed for fisheries management, Automatic Identification System (AIS) data provides useful, open access, near real-time and high-resolution information that allows for increased monitoring, particularly around Marine Protected Areas (MPAs) and in Areas Beyond National Jurisdiction. This study uses AIS data provided by Global Fishing Watch to assess the spatial distribution and seasonal dynamics of fishing effort by vessel flag within the GMR and the IEEZ from 2012 to 2021. Based on kernel density estimation analysis, we determinate the core-use areas (50%) and spatial extent (95%) of fishing activities by fleets (Ecuadorian and foreign), gear types and seasons (warm, from December to May; and cold, from June to November). Our results show that the Ecuadorian fleet recorded the most observed fishing hours in the study area, with 32,829 hours in the IEEZ and 20,816 hours within the GMR. The foreign flags with the most observed fishing hours in the IEEZ were Panama (3,245 hours) and Nicaragua (2,468.5 hours), while in the GMR were the 'Unknown flag' (4,991.4 hours) and Panama (133.7 hours). Vessels fished employing different fishing gears, but the waters of the GMR and IEEZ were mostly targeted by tuna purse-seiners and drifting longlines. The spatial distribution of the fishing effort exhibits marked seasonal variability, likely influenced by seasonal migrations of target species such as tunas (e.g., Thunnus albacares, T. obesus and Katsuwonus pelamis), marlins (e.g., Makaira nigricans) and sharks (e.g., Alopias pelagicus). The collection and use of this type of spatial and seasonal information is an essential step to understand the dynamics of fishing activities in national waters and improve fisheries management, particularly in less studied areas and fisheries.

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.

Spatial information extraction of fishing grounds for light purse seine vessels in the Northwest Pacific Ocean based on AIS data

Ecological fishery management requires high-precision fishery information to support resource management and marine spatial planning. In this paper, the Automatic Identification System (AIS) was adopted to extract the spatial information on the fishing grounds of light purse seine vessels in the Northwest Pacific Ocean. The spatial distributions of fishing grounds mapped by the data mining, kernel density analysis and hotspot analysis methods were compared. The spatial similarity index was applied to determine the spatial consistency between the computed spatial information and fisheries resource information. Finally, the spatial information derived by the best method was used to investigate the characteristics of fishing activity. The results showed that: the speed of light purse seine vessels related to operations was lower than 1.6 knots. The spatial information extracted by the three methods was consistent with the catch data distribution, and the spatial similarity between the fishing effort and catch data was the highest. The spatial variation in fishing activity was similar to that in the chub mackerel migration route. AIS data could be used to provide high-resolution fishery information. Light purse seine fishing vessels typically operate and travel along the exclusive economic zone boundary, and increased attention must be given to fishing vessel operation supervision. A comprehensive supervision system can be employed to monitor the operations of fishing vessels more effectively. The results of this study can provide technical support for the management of fishing activities and conservation of marine resources in this region using AIS data.

A novel algorithm for ship characteristic points extraction based on density clustering

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.

A novel method for extracting near-misses from AIS data involving collision avoidance behaviour

This paper presents a novel method for extracting near-misses involving collision avoidance behaviour based on automatic identification system data. Previous methods for near-misses extraction primarily focused on detecting collision risks from ship-to-ship encounter scenarios. The proposed method introduces avoidance behaviour as an additional factor for extracting near-misses. It further analyzes the subject of avoidance behaviour through the avoidance intention, aiming to extract near-misses where one ship's avoidance behaviour is prompted by another ship within the same ship-to-ship scenario. The method consists of three primary stages: (1) Detection of encounter scenarios with collision risk using the velocity obstacle method; (2) Identification of encounter scenarios with maneuvering behaviour based on course data; (3) Recognition of near-misses involving avoidance behaviour by estimating avoidance intention. Finally, the effectiveness and innovativeness of the proposed method are validated through a comparative experiment, and the influence of various parameters is thoroughly analyzed in an applied experiment.

Changes in the Arctic Traffic Occupancy and Their Connection to Sea Ice Conditions from 2015 to 2020

Arctic shipping activities are increasing in the context of sea ice decline. However, research gaps persist in studying recent Arctic shipping activities across various vessel types and their connection with sea ice conditions. Utilizing Automatic Identification System (AIS) data and sea ice satellite observations between 2015 and 2020, these matters are delved into this study. A discernible overall growth trend in Arctic traffic occupancy occurs from 2015 to 2020 during summer and autumn. Excluding passenger ships, the traffic occupancy trend for each ship type closely parallels that for all ships. Variations in traffic occupancy along the Northeast Passage dominate that in the entire Arctic. As sea ice diminishes, both Arctic traffic occupancy and its variability noticeably increase. Further examination of the relationship between shipping activities and ice conditions reveals that increased traffic occupancy corresponds significantly to diminishing sea ice extent, and the constraint imposed by sea ice on Arctic traffic occupancy weakens, while the 6-year AIS data could lead to uncertainties. In summary, as the Arctic sea ice declines continuously, not only sea ice but also additional social, military, and environmental factors constraining marine activities should be considered in the future operation of Arctic shipping.

Structure and resilience changes of global liquefied natural gas shipping network during the Russia–Ukraine conflict

Research on the structure and resilience of marine networks that transport liquefied natural gas (LNG) can inform policies to secure global energy supplies. Since the 1990s, geopolitical risks have been crucial in the LNG supply chain. However, existing research inadequately addresses the impact of major geopolitical events, such as the Russia–Ukraine conflict, on LNG maritime networks. This study utilized 2021–2022 Automatic Identification System (AIS) data to create a global LNG maritime network, including ports and chokepoints, to assess changes in structure and resilience during the Russia–Ukraine conflict. We also simulated changes in network resilience under various attack scenarios. The findings indicate that the Russia–Ukraine conflict has impacted the direction and volume of LNG flows. Structural changes were observed, including changes in the weighted degree of nodes and the composition of port communities. In 2022, the network demonstrated dynamic resilience, invulnerability, and recoverability changes, with noticeable self-healing properties. However, these capabilities were lower than those in 2021. Intentional attacks on maritime chokepoints, such as the Strait of Malacca, result in a much faster decline in network resilience than random attacks. These findings suggest that the Russia–Ukraine conflict has altered the resilience and structure of LNG maritime networks. Countries heavily dependent on LNG should prioritize expanding their LNG-receiving terminals and diversifying their LNG sourcing strategies.

Terminal Congestion Analysis of Container Ports Using Satellite Images and AIS

This study proposes the use of satellite images and a vessel’s automatic identification system (AIS) data to evaluate the congestion level at container ports for operational efficiency analysis, which was never attempted in previous studies. The congestion level in container yards is classified by developing a convolutional neural network (CNN) model and an annotation tool to reduce the workload of creating training data. The annotation tool calculates the number of vertically stacked containers and the reliability of each container cell in a detection area by focusing on the shadows generated by the containers. Subsequently, a high-accuracy CNN model is developed for end-to-end processing to predict congestion levels. Finally, as an example of dynamic efficiency analysis of container terminals using satellite images, the relationship of the estimated average number of vertically stacked containers in the yard with the elapsed time between the image capture time and vessel arrival or departure time obtained from the automatic identification system data is analyzed. This study contributes to representing a prototype for dynamically estimating the number of vertically stacked containers and congestion level of container terminals using satellite images without statistical information, as well as its relationship with the timing of vessel arrival acquired from AIS data.

An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data

Ship trajectory prediction is essential for ensuring safe route planning and to have advanced warning of the dangers at sea. With the development of deep learning, most of the current research has explored advanced prediction methods based on historical spatio-temporal Automatic Identification System (AIS) data. However, environmental factors such as sea wind and visibility also affect ship navigation in real-world maritime shipping. Therefore, developing reliable models utilizing multimodal data, such as AIS and environmental data, is challenging. In this research, we design an adaptive multimodal vessel trajectory data prediction model (termed AMD) based on satellite AIS and environmental data. The AMD model mainly consists of an AIS-based extraction network, an environmental-based extraction network, and a fusion block. In particular, this work considers multimodal data such as historical spatio-temporal information and environmental factors. Time stamps and distances are correlated with AIS and environmental data, and a multilayer perceptron and gated recurrent unit networks are used to design multimodal feature extraction networks. Finally, the fusion block realizes the fusion output of multimodal features to improve the reliability of the AMD model. Several quantitative and qualitative experiments are conducted using real-world AIS and multimodal environmental datasets. Numerous experimental results prove that prediction performance using multimodal data can ensure satisfactory accuracy and reliability while exhibiting a positive impact on improving maritime transport services.

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.

A hybrid deep learning method for the prediction of ship time headway using automatic identification system data

Ship Time Headway (STH) is used in maritime navigation to describe the time interval between the arrivals of two consecutive ships in the same water area. This measurement may offer a straightforward way to gauge the frequency of ship traffic and the likelihood of congestion in a particular area. STH is an important factor in understanding and managing the dynamics of ship movements in busy waterways. This paper introduces a hybrid deep learning method for predicting STH in time domain. The method integrates the Seasonal-Trend Decomposition using Loess (STL), Multi-head Self-Attention (MSA) mechanism into Long Short-Term Memory (LSTM) neural network. The STH dataset was extracted from the Automatic Identification System (AIS) through ship trajectory spatial motion, and the seasonal, trend and residual components of the decomposition were then determined from the STH dataset using the STL algorithms. MSA-LSTM is adopted to comprehensively capture the evolving patterns of STH from the sequence. Comparison studies with existing methods demonstrate the accuracy and robustness of the predictions provided by this method, indicating that the proposed method outperforms other models in terms of prediction performance and learning capabilities. By predicting STH, the method offers potential to assist maritime traffic managers and navigators in assessing ship flow, thereby enabling them to make informed decisions on navigation safety and efficiency.

Unseen overlap between fishing vessels and top predators in the northeast Pacific.

Accurate assessments of human-wildlife risk associated with industrial fishing are critical for the conservation of marine top predators. Automatic Identification System (AIS) data provide a means of mapping fishing and estimating human-wildlife risk; however, risk can be obscured by gaps in the AIS record due to technical issues and intentional disabling. We assessed the extent to which unseen fishing vessel activity due to AIS gaps obscured estimates of overlap between fishing vessel activity and 14 marine predators including sharks, tunas, mammals, seabirds, and critically endangered leatherback turtles. Among vessels equipped with AIS in the northeast Pacific, up to 24% of total predator overlap with fishing vessel activity was unseen, and up to 36% was unseen for some individual species. Waters near 10°N had high unseen overlap with sharks yet low reported shark catch, revealing potential discrepancies in self-reported datasets. Accounting for unseen fishing vessel activity illuminates hidden human-wildlife risk, demonstrating challenges and solutions for transparent and sustainable marine fisheries.

A big data analytics method for the evaluation of maritime traffic safety using automatic identification system data

The complex traffic situations are among the factors influencing maritime safety. They can be quantitatively estimated through the analysis of traffic data. This paper explores the impact of complex traffic situations on maritime safety, focusing on inland waterway traffic. It presents a big data analytics method, utilizing data from the Automatic Identification System (AIS) and historical maritime accident records. The methodology involves AIS data preprocessing and spatial autocorrelation models, including Moran's index, to extract and evaluate the dynamic characteristics of maritime traffic. The analysis of traffic characteristic includes a thorough investigation into the spatial-temporal distribution of ship average speed and trajectory density. The paper then introduces an effective traffic characteristic analysis model that evaluates the relationship between maritime traffic patterns and accidents. The study, specifically targeting the Nanjing section of the Yangtze River, reveals variations in ship trajectory density and average speed over time. It identifies several hotspots with a significant local correlation between these factors. Moreover, a substantial correlation is found between the locations of maritime accidents and areas with increased ship trajectory density and average speed. These results may provide insights for traffic safety management and highlight strategies for preventing maritime accidents.

Optimising port arrival statistics: Enhancing timeliness through Automatic Identification System (AIS) data

Today, there is a greater demand to produce more timely official statistics at a more granular level. National Statistical Institutes (NSIs) are more and more looking to novel data sources to meet this demand. This paper focuses on the use of one such source to compile more timely and detailed official statistics on port visits. The data source used is sourced from the Automatic Identification System (AIS) used by ships to transmit their position at sea. The primary purpose of AIS is maritime safety. While some experimental statistics have been compiled using this data, this paper evaluates the potential of AIS as a data source to compile official statistics with respect to port visits. The paper presents a novel method called “Stationary Marine Broadcast Method” (SMBM) to estimate the number of port visits using AIS data. The paper also describes how the H3 Index, a spatial index originally developed by Uber, is added to each transmission in the data source. While the paper concludes that the AIS based estimates won’t immediately replace the official statistics, it does recommend a pathway to using AIS-based estimates as the basis for official port statistics in the future.