Automatic Identification Systems Data Research Articles

A New Classification Method for Ship Trajectories Based on AIS Data

Automatic identification systems (AIS) can record a large amount of navigation information about ships, including abnormal or illegal ship movement information, which plays an important role in ship supervision. To distinguish the trajectories of ships and analyze the behavior of ships, this paper adopts the method of supervised learning to classify the trajectories of ships. First, the AIS data for the ships were marked and divided into five types of ship tracks. The Tsfresh module was then used to extract various ship trajectory features, and a new ensemble classifier based on traditional classification using a machine learning algorithm was proposed for modeling and learning. Moreover, ten-fold cross validation was used to compare the ship trajectory classification results. The classification performance of the ensemble classifier was better than that of the other single classifiers. The average F1 score was 0.817. The results show that the newly proposed method and the new ensemble classifier have good classification effects on ship trajectories.

Shining a light on AIS Blackouts with maritime OSINT

Maritime open source intelligence (maritime OSINT) can be used to capture information, critical to business operations, including when electronic systems like Automatic Identification Systems (AIS) fail. AIS is used to identify vessels in maritime navigation, to plan shipping operations, and ensure safety. This data is collected and sold by global aggregators to maritime operators and industry stakeholders. However, AIS communications are vulnerable to malicious cyber activity, changing legal landscapes, adverse physical conditions, or intentional intervention. It is the latter we will focus on in this report. Often, we won't understand the motivation behind a decision to either switch off or manipulate AIS data. However, this decision can have real world impacts, including on relationships between nation states, and the physical safety of maritime personnel. We believe maritime OSINT may represent an effective method to assist in the management of AIS failures. Maritime OSINT not only captures important maritime information when AIS fails, but it provides valuable context, giving us the who, what, and why for vessel operations. In our cast study, we will use maritime OSINT to track sanctioned Russian oil exports, demonstrating how OSINT can be used in this example to inform sanctions compliance programs.

Data association for autonomous ships based on virtual simulation environment

Ships obtain information regarding their surroundings using various types of sensors such as automatic identification systems (AISs), radio detection and ranging (RADAR), and cameras. Each sensor data point includes different information and exhibits different characteristics, and the measurement data generated from a single target must be associated to increase the recognition accuracy and tracking consistency. Whereas AIS data include the maritime mobile service identity, which is a unique identifier for ships, the information of ships detected by RADAR or cameras is limited; thus, an identifier must be assigned through data association. In this study, an improved data-association algorithm, which uses the coherent point drift and nearest neighbor methods, is developed. Additionally, the data-association algorithm is verified using actual sensor data generated during ship navigation. A virtual simulation environment is constructed to generate sensor data for the development and verification of the data-association algorithm. Subsequently, target ships are detected from virtual cameras and RADAR images, and the visual information of the ships is extracted from the camera images. Subsequently, the measurement data are associated using the position, speed, and length of the target ships to improve the accuracy of the data association. Finally, the applicability of the proposed algorithm for data association is verified via a virtual simulation. Results show that the proposed algorithm can be applied to practical scenarios involving severe conditions for data association.

Ship Trajectory Clustering Based on Trajectory Resampling and Enhanced BIRCH Algorithm

Automatic identification systems (AIS) provides massive ship trajectory data for maritime traffic management, route planning, and other research. In order to explore the valuable ship traffic characteristics contained implicitly in massive AIS data, a ship trajectory clustering method based on ship trajectory resampling and enhanced BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) algorithm is proposed. The method has been tested using 764,393 AIS trajectory points of 13,845 ships in the waters of the Taiwan Strait of China, and 832 ship trajectories have been generated and clustered to obtain 172 classes of ship trajectory line clusters among 40 port pairs. The experimental results show that the proposed method has exhibited a good clustering effect on ship trajectories. Compared with the existing ship trajectory clustering methods, the proposed method can more efficiently detect and identify differences between trajectories with largely similar spatial distribution characteristics, so as to obtain legitimate clustering results. In addition, this study has constructed the main ship navigation routes between ports based on the extracted ship trajectory line clusters, and the constructed main routes are directional, refined, and rich in content compared with the existing ship routes. This research provides theoretical and technical support for ship route planning and maritime traffic management.

Trajectory Data Compression Algorithm Based on Ship Navigation State and Acceleration Variation

An active area of study under the dual carbon target, which is based on automatic identification systems (AIS), is the emission inventory of pollutants from ships. Data compression is required because there is currently so much data that it has become difficult to transmit, process, and store it. A trajectory simplification method considering the ship sailing state and acceleration rate of change is developed in this paper to assure the validity of the compressed data used in the emission inventory analysis. By carefully examining the integral relationship between acceleration and pollution emissions, the algorithm constructs an acceleration rate of change function for data compression and categorizes AIS data by ship navigation status. By dynamically altering the amount of acceleration change, the developed function can stabilize the pollutant emission calculation error and adaptively calculate the threshold value. The experimental results show that the emission calculation error of the proposed algorithm is only 0.185% when the compression rate is 90.28%.

A Deep Learning Method for NLOS Error Mitigation in Coastal Scenes

With the widespread use of automatic identification systems (AISs), some ships use deceptive information or intentionally close their AISs to conceal their illegal activities or evade the supervision of maritime departments. Although radar measurements can be effectively utilized to evaluate the credibility of received AIS data, the propagation of non-line-of-sight (NLOS) signal conditions is an important factor that affects location accuracy. This study addresses the NLOS problem in a special geometric dilution of precision (GDOP) scenario on a coast and several base stations. We employed data augmentation and a deep residual shrinkage network in order to alleviate the adverse effects of NLOS errors. The results of our simulations demonstrate that the proposed method outperforms other range-based localization algorithms in a mixed LOS/NLOS environment. For a special GDOP scenario with four radars, our algorithm’s root-mean-square error (RMSE) was lower than 180 m.

Vessel manoeuvring hot zone recognition and traffic analysis with AIS data

Automatic identification systems (AIS) are on-board compulsory devices on ships that can provide a large amount of dynamic vessel information, such as position, course and speed information, which is meaningful for marine management. This paper explores the potential to utilize AIS data to recognize ship manoeuvring hot zones and analyse their characteristics to improve marine management efficiency. First, considering manoeuvring behaviours such as turning, acceleration and deceleration, a novel approach based on MiniBatchKMeans that recognizes ship turning hot zones, acceleration hot zones and deceleration hot zones is proposed according to course and speed variation. Second, joint probability density functions for speed over ground (SOG) and course over ground (COG) are built taking into account the combined influences of motion characteristics, which are beneficial for determining the ship traffic rules in manoeuvring hot zones and utilized in marine management. Finally, numerical and comparison experiments are conducted to test the feasibility and effectiveness of the proposed method.

Towards a Secure and Scalable Maritime Monitoring System Using Blockchain and Low-Cost IoT Technology

Maritime Domain Awareness (MDA) is a strategic field of study that seeks to provide a coastal country with an effective monitoring of its maritime resources and its Exclusive Economic Zone (EEZ). In this scope, a Maritime Monitoring System (MMS) aims to leverage active surveillance of military and non-military activities at sea using sensing devices such as radars, optronics, automatic Identification Systems (AISs), and IoT, among others. However, deploying a nation-scale MMS imposes great challenges regarding the scalability and cybersecurity of this heterogeneous system. Aiming to address these challenges, this work explores the use of blockchain to leverage MMS cybersecurity and to ensure the integrity, authenticity, and availability of relevant navigation data. We propose a prototype built on a permissioned blockchain solution using HyperLedger Fabric—a robust, modular, and efficient open-source blockchain platform. We evaluate this solution’s performance through a practical experiment where the prototype receives sensing data from a Software-Defined-Radio (SDR)-based low-cost AIS receiver built with a Raspberry Pi. In order to reduce scalability attrition, we developed a dockerized blockchain client easily deployed on a large scale. Furthermore, we determined, through extensive experimentation, the client optimal hardware configuration, also aiming to reduce implementation and maintenance costs. The performance results provide a quantitative analysis of the blockchain technology overhead and its impact in terms of Quality of Service (QoS), demonstrating the feasibility and effectiveness of our solution in the scope of an MMS using AIS data.

Detection of abnormal ship trajectory based on the complex polygon

AbstractShip anomaly detection is a vital aspect for monitoring navigational safety in specific water areas. Considering the effect of water channel boundaries, we propose the detection of an abnormal ship trajectory based on the complex polygon (DATCP) method to detect ship anomalies in this study. With the automatic identification systems (AIS) data from the Yangtze River estuary, a case study is created to verify the effectiveness of the proposed DATCP method. The case study results reveal that the proposed DATCP method can provide higher detection accuracy than the conventional A* algorithm. The feature analysis results indicate that ship anomalies are significantly influenced by ship type, time period, weather conditions and ship traffic characteristics.

Evaluating Adherence With Voluntary Slow Speed Initiatives to Protect Endangered Whales

Vessel strikes are one of the main threats to large whales globally and to endangered blue, fin, and humpback whales in California waters. For over 10 years, NOAA has established seasonal voluntary Vessel Speed Reduction (VSR) zones off of California and requested that all vessels 300 gross tons (GT) or larger decrease speeds to 10 knots or less to reduce the risk of vessel strikes on endangered whales. We offer a comprehensive analysis quantifying cooperation levels of all vessels ≥ 300 GT from 2010 to 2019 within designated VSR zones using Automatic Identification Systems (AIS) data. While average speeds of large vessels have decreased across the years studied, cooperation with voluntary 10-knot speed reduction requests has been lower than estimated to be needed to reduce vessel-strike related mortality to levels that do not inhibit reaching and maintaining optimal sustainable populations. A comparison of vessel speeds across inactive and active voluntary VSR time periods show a modest (+ 15%) increase in cooperation from 2017 to 2019. A complementary, incentive-based VSR program that was started in 2014 and scaled up in 2018 within the region likely improved voluntary VSR cooperation levels, as participating container and car carrier vessels traveled at lower speeds during the VSR season than vessels not enrolled in the incentive-based effort. Comparisons of vessel speeds in the incentive-based VSR program across inactive and active time periods showed a significant (+ 41%) increase in cooperation. With cooperation levels for the voluntary VSR hovering around 50%, and the challenge of funding and sustaining an incentive-based VSR program, voluntary VSR approaches may be insufficient to achieve cooperation levels needed to significantly reduce the risk of vessel strike-related mortality for these federally protected whales, suggesting that VSR regulations warrant consideration.

Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data

With the increasing application and utility of automatic identification systems (AISs), large volumes of AIS data are collected to record vessel navigation. In recent years, the prediction of vessel trajectories has become one of the hottest research issues. In contrast to existing studies, most researchers have focused on the single-trajectory prediction of vessels. This article proposes a multiple-trajectory prediction model and makes two main contributions. First, we propose a novel method of trajectory feature representation that uses a hierarchical clustering algorithm to analyze and extract the vessel navigation behavior for multiple trajectories. Compared with the classic methods, e.g., Douglas–Peucker (DP) and least-squares cubic spline curve approximation (LCSCA) algorithms, the mean loss of trajectory features extracted by our method is approximately 0.005, and it is reduced by 50% and 30% compared to the DP and LCSCA algorithms, respectively. Second, we design an integrated model for simultaneous prediction of multiple trajectories using the proposed features and employ the long short-term memory (LSTM)-based neural network and recurrent neural network (RNN) to pursue this time series task. Furthermore, the comparative experiments prove that the mean value and standard deviation of root mean squared error (RMSE) using the LSTM are 4% and 14% lower than those using the RNN, respectively.

Fishing Gear Pattern Recognition by Including Supervised Autoencoder Dimensional Reduction

Fishing is a crucial worldwide activity as it provides a source of food and economic income. A challenge in ecology and conservation is decreasing overfishing and illegal, unreported, and unregulated fishing (IUUF). One strategy to decrease those issues is to track vessels for detecting fishing behaviors through monitory systems. In this letter, we present an approach to classify fishing behaviors, specifically, for four fishing gear types (trawl, purse seine, fixed gear, and longline) using automatic identification systems (AISs) data from the Global Fishing Watch platform. Thus, our main contribution is how we propose data processing by including a supervised autoencoder dimensional reduction (SA-DR) processing data step. This step allows removing redundant features and noise, avoiding overfitting, decreasing data complexity, and preserving the differences between classes. Specifically, we propose to use IVIS and centroid encoder (CE) methods. The experimental results show how our approach applying SA-DR over the vessel trajectory feature representation reduces the variation results among different classifiers and achieves a high classification accuracy of up to 95%. This result could help prevent IUUF, overfishing, and improve fishery management strategies.

Marine Traffic Data Clustering for Ships Route Planning

This paper is about maritime safety. The system of vessel traffic schemas is one of the key elements of sea traffic control at the arias with heavy traffic. Such system based on a set of rules and guidelines defined by traffic schemas for certain water areas. From the classic approach, vessels that are not following the guidelines do not necessarily create alarming situations at the moment, however, could lead to complex danger navigation situations with the time passed. The problem of ship route planning through the area with highly intensive traffic is considered in this paper. The importance of the problem becomes more significant these days when taking in account development of self-navigating autonomous vessels. It is expected to respect area navigation limitations while planning vessel path through the areas with identified traffic schema. One of the ways to identify navigation limitations could be trajectory pattern recognition at certain sea areas based on retrospective traffic analysis. Model representation for such task could be based on vessel moving parameters clustering. The presented model is based on solving the shortest path problem on weighted graph. There are several ways to create such weighted graphs are suggested in the paper: regular grid of vertices and edges, layer grid of vertices and edges, random grid of vertices and edges, vertices and edges identified based on retrospective data. All edges are defined as a weighted function of "desirability" of one or another vessel course for each location of sea area with consideration of identified trajectory patterns. For that the area is divided into sub areas where courses and velocity clustering is evaluated. Possible ways of clustering are discussed in the paper and the choice made in favor of subtractive clustering that does not require predefining of cluster count. Automatic Identification Systems (AIS) could be used as data source for the traffic at certain sea areas. The possibility of using AIS data available on specialized public Internet resources is shown in the paper. Although such data typically has low density, they still could well represent vessel traffic features at the certain sea area. In this paper are presenting samples of route panning for Tsugaru Straight ang Tokyo Bay.

MVFFNet: Multi-view feature fusion network for imbalanced ship classification

The accurate classification of moving ships is of fundamental importance to maritime authorities for ensuring the safety and security of shipping operations. With the wide use of automatic identification systems (AISs), which allow ships to receive identification/location information from nearby ships, it is feasible to classify the types of ships by analysing ship behaviour from AIS-based trajectories. However, the imbalanced features of AIS data make it difficult to achieve satisfactory classification results in the presence of several different types of ships. To overcome these potential limitations, we propose a multi-view feature fusion network (MVFFNet) to achieve accurate ship classification with imbalanced data. To guarantee the powerful representation and generalization abilities of MVFFNet, we first extract several multi-view features (i.e., motion features and morphological features) from AIS-based ship trajectories. Several kinematic variables related to ship behaviour are empirically adopted as motion features. The morphological features are automatically extracted via convolutional auto-encoder (CAE) networks. CAE networks are capable of optimally learning the features from informative trajectory images, which are strictly related to the original ship trajectories. The bidirectional gated recurrent unit (BiGRU) network is then proposed to combine multi-view features to generate the ship classification results. In addition, a hybrid loss function is presented to handle the imbalance problem of ship types, potentially leading to enhanced robustness and accuracy of ship classification. Comprehensive experiments on two realistic datasets have demonstrated that our proposed MVFFNet consistently outperforms other competing methods in terms of classification accuracy and robustness.

Interactions between Arctic passenger ship activities and emissions

• This paper uses the Automatic Identification Systems (AIS) data to analyze passenger ship trajectories and sailing speed in the Arctic during 2012–2017. • The study adopts passenger ship data (2012–2017) together with the robust emission model to calculate the pollution emission inventory in the Arctic. • Passenger ships are mainly concentrated on the Svalbard Archipelago and Greenland. Starting from 2012 to 2017, the highest annual passenger ship emissions occurred in July and August. • The study evaluates CO 2 equivalent emissions from different shipping fuel options. The ice melt in the Arctic region has generated a great opportunity for passenger ships to sail in the Arctic. As such, a significant growth of passenger ship activities in this region has created an ever-increasing air pollution, which has had an adverse effect on the maritime Arctic ecosystem. Due to the fragility of the Arctic marine environment, it is critical to conduct a thorough analysis of the passenger ship activities in the Arctic and assess their environmental effects on the Arctic. This paper uses the Automatic Identification Systems (AIS) data to analyze passenger ship trajectories and sailing speed in the Arctic from 2012 to 2017. Based on the passenger ship characteristics data combined with the bottom-up pollution emission models, we quantify passenger ship pollutants in the Arctic. Furthermore, we evaluate the CO 2 equivalent emissions for the four shipping fuel options of interest. New findings include: (1) Passenger ships have discharged an average of 39.17 tons of black carbon (BC) and 3824.01 tons of SOx in the Arctic each year (i.e., from 2012 to 2017); (2) Among the four operating modes, cruising exhibited the largest amount of BC and SOx emissions, followed by berthing, anchoring, and maneuvering; (3) The amounts of BC and SOx emitted from auxiliary engines (AEs) were the highest, followed by main engines (MEs) and boilers (BOs); and (4) Arctic passenger ship emissions have shown significant monthly, daily and hourly variations. Besides, the results show that in the short term, passenger ships using very low Sulphur fuel oil (VLSFO) in the Arctic are the most viable and environmental-friendly, whereas in the long term, passenger ships using liquefied natural gas (LNG) would be even better. This investigation provides a valuable set of insights for passenger ship operators, policymakers, and scientists to design and implement future passenger ship activities in the Arctic. As expected, a control of pollution emissions can be improved, and necessary actions should be taken in the future.

Realising advanced risk assessment of vessel traffic flows near offshore wind farms

Offshore wind farms (OWFs) are relatively new installations at sea. Accident records related to vessel collisions with OWFs are insufficient to support a full quantitative risk analysis using traditional probabilistic approaches. This paper aims to develop a semi-qualitative risk model to assess the vessel-turbine collision risks by incorporating Bayesian networks (BN) with evidential reasoning (ER) approaches. First, a BN is trained based on Automatic Identification Systems (AIS) data to characterise real vessel traffic flows, including the detailed information and relationships between traffic flow parameters. Secondly, through synthesising expert judgements by ER, five risk factors influencing the probability and consequence of vessel-turbine collisions are identified (incl. the associated conditional probabilities) in the established BN. Finally, the updated BN with ER input is tested through ten real scenarios and validated by processing a validity framework. This paper pioneers the use of multi-data-driven BNs to characterise traffic flows and assess vessel-turbine collision risk for navigational safety assurance near OWFs. The research findings provide empirical evidence of using ER to supplement BN subjective data to advance its applications in risk analysis.

Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data

Preventing and controlling the risk of importing the coronavirus disease (COVID-19) has rapidly become a major concern. In addition to air freight, ocean-going ships play a non-negligible role in spreading COVID-19 due to frequent visits to countries with infected populations. This research introduces a method to dynamically assess the infection risk of ships based on a data-driven approach. It automatically identifies the ports and countries these ships approach based on their Automatic Identification Systems (AIS) data and a spatio-temporal density-based spatial clustering of applications with noise (ST_DBSCAN) algorithm. We derive daily and 14 day cumulative ship exposure indexes based on a series of country-based indices, such as population density, cumulative confirmed cases, and increased rate of confirmed cases. These indexes are classified into high-, middle-, and low-risk levels that are then coded as red, yellow, and green according to the health Quick Response (QR) code based on the reference exposure index of Wuhan on April 8, 2020. This method was applied to a real container ship deployed along a Eurasian route. The results showed that the proposed method can trace ship infection risk and provide a decision support mechanism to prevent and control overseas imported COVID-19 cases from international shipping.

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

Timely and accurate depth estimation of a shallow waterway can improve shipping efficiency and reduce the danger of waterway transport accidents. However, waterway depth data measured during actual maritime navigation is limited, and the depth values can have large variability. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate waterway depths, although these data include no direct channel depth information. We suggest a deep neural network (DNN) based model, called DDTree, for using the real-time AIS data and the data from Global Mapper to predict waterway depth for ships in an accurate and timely way. The model combines a decision tree and DNN, which is trained and tested on the AIS and Global Mapper data from the Nantong and Fangcheng ports on the southeastern and southwestern coast of China. The actual waterway depth data were used together with the AIS data as the input to DDTree. The latest data on waterway depths from the Chinese maritime agency were used to verify the results. The experiments show that the DDTree model has a prediction accuracy of 91.15%. Therefore, the DDTree model can provide an accurate prediction of waterway depth and compensate for the shortage of waterway depth monitoring means. The proposed hybrid DDTree model could improve marine situational awareness, navigation safety, and shipping efficiency, and contribute to smart navigation.

Using Satellite AIS to Analyze Vessel Speeds Off the Coast of Washington State, U.S., as a Risk Analysis for Cetacean-Vessel Collisions

Most species of whales are vulnerable to vessel collisions, and the probability of lethality increases logistically with vessel speed. Spatially explicit risk assessments can inform the marine management process about the potential for vessel collisions. We used Satellite Automatic Identification Systems (SAIS) data from 2013 and 2014 to calculate vessel speed over ground around the Olympic Coast National Marine Sanctuary. Nearby shipping lanes connecting the Ports of Vancouver, Seattle, Tacoma, and Portland have the greatest density of vessel traffic arriving and departing, and these densely traveled routes continue outside the US Exclusive Economic Zone. We characterized speed and density based on vessel type and for areas of interest, including NOAA’s Cetacean Density and Distribution Working Group’s Biologically Important Areas. Cargo and tanker vessels constitute the majority of distance traveled at the greatest speeds. We found that calculated speed based on SAIS is higher and less variable than broadcast speed for most vessel types. It is important to use calculated speed over ground (SOG) in any analysis so that the risk from potential vessel collisions is not underestimated by using broadcast SOG. Temporal gaps in the SAIS data led to a resulting systematic underestimation of vessel speed in calculated speed over ground. In that case, SAIS can be helpful in documenting minimum vessel speeds across large geographic areas, especially beyond the reach of terrestrial AIS receivers. Satellite AIS is useful in examining vessel density at broad scales, and could be used to assess basin-wide open ocean routes. Future use of additional satellite platforms with AIS receivers will only increase the quality of SAIS data and decrease the amount of temporal gaps.

The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation

The member states of International Maritime Organization (IMO) have been leading in and enforcing the use of automatic identification systems (AIS) in the analysis of ship-to-ship collisions, vessel monitoring, and maritime traffic management offshore. This study will help non-federal stakeholders understand the AIS data and contribute to future research by assessing difficulties and improving access to data and applications. This study introduces the basics of AIS materials, shared channels, and currently developed applications, and discusses areas where they can be incorporated in the future. The literature revealed that using AIS data will be beneficial to the public as well as to business and public agencies.