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Monitoring vessel activity is an important part of managing marine protected areas (MPAs), but small-scale fishing and recreational vessels that do not participate in cooperative vessel traffic systems require additional monitoring strategies. Marine Monitor (M2) is a shore-based, multi-sensor platform that integrates commercially available hardware, primarily X-band marine radar and optical cameras, with custom software to autonomously track and report on vessel activity regardless of participation in other tracking systems. By utilizing established commercial hardware, the radar system is appropriate for supporting the management of coastal, small-scale MPAs. Data collected in the field are transferred to the cloud to provide a continuous record of activity and identify prohibited activities in real-time using behavior characteristics. To support the needs of MPA managers, both hardware and software improvements have been made over time, including ruggedizing equipment for the marine environment and powering systems in remote locations. Case studies are presented comparing data collection by both radar and the Automatic Identification System (AIS) in urban and remote locations. At the South La Jolla State Marine Reserve near San Diego, CA, USA, 93% of vessel activity (defined as the cumulative time vessels spent in the MPA) was identified exclusively by radar from November 2022 through January 2023. At the Caye Bokel Conservation Area, within the Turneffe Atoll Marine Reserve offshore of Belize, 98% was identified exclusively by radar from April through October 2022. Spatial and temporal patterns of radar-detected and AIS activity also differed at both sites. These case study site results together demonstrate the common and persistent presence of small-scale vessel activity near coastal MPAs that is not documented by cooperative systems. Therefore, an integrated radar system can be a useful tool for independent monitoring, supporting a comprehensive understanding of vessel activity in a variety of areas.

High-frequency surface wave radar (HFSWR) can detect and continuously track ship targets in real time and beyond the horizon. Compared with transmit/receive (T/R) monostatic HFSWR, the T-R bistatic HFSWR has the advantages of flexibility, receiver concealment, and large coverage because of the separation between the radar transmitter and receiver locations. So far, there is little research on marine ship monitoring for bistatic HFSWR. This article examines ship monitoring performance with T-R bistatic compact HFSWR of small aperture based on measured experimental data. The first-order sea-clutter characteristics and detection blind zone for T-R bistatic HFSWR were investigated theoretically, and the formulas for the moving ships were derived and its position accuracy were analyzed from simulation results. The experimental results of ship target detection of bistatic compact HFSWR carried out in 2015 were presented, and the spectrum characteristics of the bistatic HFSWR were analyzed with measured experimental data. A method integrating detection and tracking was applied to the target detection data collected by the bistatic compact radar, and two targets examples tracked were given. Finally, the validity of the method and the tracing results were verified by using synchronous automatic identification system data, and the ship positioning accuracy of the bistatic compact HFSWR was statistically analyzed. The analysis shows that bistatic radars have larger errors than monostatic T/R radars, with a positioning error as large as 10 km.

ObjectivesThe near-miss of two ships is a special situation that ships will encounter, and it can be modeled in terms of the time-dimension perspective for evaluating potential maritime traffic risks.Methods In this paper, a temporal near-miss model is first established using the Fujii ship domain to detect near-miss situations, and the near-miss duration and average duration are then defined. An algorithm for calculating near-miss duration is proposed on the basis of the time synchronization processing of automatic identification system (AIS) data. Furthermore, the near-miss duration is used to analyze the time-dimension characteristics of maritime traffic risks. The Bohai Sea area is used as the research example to verify the model.ResultsThe correlations between the average near-miss duration and ship type and length are determined. According to ship type analysis, the longest average near-miss duration is 252.63 s for an oil tanker and at least 180 s for a passenger ship. According to ship size analysis, the longest average near-miss duration is 272.65 s for a large ship and at least 180 s for a mid-size ship.ConclusionsThe proposed model can be used to accurately calculate near-miss duration, providing a new approach to studying maritime traffic risks from the time-dimension perspective.

The rapid development and adoption of automatic identification systems as surveillance tools have resulted in the widespread application of data analysis technology in maritime surveillance and route planning. Traditional, manual, experience-based route planning has been widely used owing to its simplicity. However, the method is heavily dependent on officer experience and is time-consuming. This study aims to extract shipping routes using unsupervised machine-learning algorithms. The proposed three-step approach: maneuvering point detection, waypoint discovery, and traffic network construction was used to construct a maritime traffic network from historical AIS data, which quantitatively reflects ship characteristics by ship length and ship type, and can be used for route planning. When the constructed maritime traffic network was compared to the macroscopic ship traffic flow, the Symmetrized Segment-Path Distance (SSPD) metric returned lower values, indicating that the constructed traffic network closely resembles the routes ships transit. The result indicates that the proposed approach is effective in extracting a route from the maritime traffic network.

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.

Maritime surveillance of the Arctic region is of growing importance as shipping, fishing and tourism are increasing due to the sea ice retreat caused by global warming. Ships that do not identify themselves with a transponder system, so-called dark ships, pose a security risk. They can be detected by SAR satellites, which can monitor the vast Arctic region through clouds, day and night, with the caveat that the abundant icebergs in the Arctic cause false alarms. We collect and analyze 200 Sentinel-1 horizontally polarized SAR scenes from areas with high maritime traffic and from the Arctic region with a high density of icebergs. Ships and icebergs are detected using a continuous wavelet transform, which is optimized by correlating ships to known AIS positions. Globally, we are able to assign 72% of the AIS signals to a SAR ship and 32% of the SAR ships to an AIS signal. The ships are used to construct an annotated dataset of more than 9000 ships and ten times as many icebergs. The dataset is used for training several convolutional neural networks, and we propose a new network which achieves state of the art performance compared to previous ship–iceberg discrimination networks, reaching 93% validation accuracy. Furthermore, we collect a smaller test dataset consisting of 424 ships from 100 Arctic scenes which are correlated to AIS positions. This dataset constitutes an operational Arctic test scenario. We find these ships harder to classify with a lower test accuracy of 83%, because some of the ships sail near icebergs and ice floes, which confuses the classification algorithms.

In view of the continuous increase in the amount of AIS data at sea and the existence of more abnormal points, it is difficult to construct ship trajectories based on AIS data. Aiming at this problem, a new method for identifying and repairing abnormal points in trajectories only based the AIS data of the ship itself is proposed. Longitude and latitude, speed, acceleration, direction and other parameters are comprehensively used to identify and repair the abnormal points in the method proposed. Compared with the methods based on single location data, it can effectively reduce the missed judgement of outliers. Compared with the methods based on trajectories clustering to judge singular point, this method does not require the data of historical trajectories to expand the application scope. The cubic spline method is used to interpolate points for the discontinuous segments to further improve the continuity and integrity of the ship trajectory. The results of AIS data processing and analysis on ships in actual sea areas verify the feasibility and effectiveness of the proposed method.

To achieve environmental sustainability on ships, stakeholders should make efforts to reduce emissions. Port authorities are crucial to attain this goal by introducing new policies. This study takes the Port of Long Beach as an example to assess port-wide ship emissions and explain the significance of shore power policy. Additionally, the study considers the impact of disruptions, such as the COVID pandemic, on ship emissions. The analysis compares data from three years before and after the pandemic to examine the relationship between ship waiting times, quantities, and emissions. The findings indicate that the majority of port-wide ship emissions are generated by berthing or anchoring vessels, from ship auxiliary engines and boilers. Furthermore, ship congestion due to reduced port productivity during the pandemic significantly increased emissions from berthing and anchoring vessels, with the emission proportion increasing from 68% to 86%. Adopting the shore power policy has effectively reduced ship emissions in port areas, and increasing the number of ships utilising shore power will be instrumental in tackling excessive ship emissions. Nanyang Technological University Published version Project 04SBS000097C120 at Nanyang Technological University, Singapore.

Global concerns regarding air quality have over the past decade led to the introduction of regulations by the International Maritime Organisation curbing the emissions of sulphur and nitrogen oxides (SOx, NOx). These limits were implemented initially in so-called “emission control areas”, defined where the density of shipping activity combines with large coastal population centres such as northwest Europe or eastern USA. However, any legislation requires a scientifically robust and rigorous monitoring program to ensure compliance and prove attribution to an individual vessel. We argue the case for adherence to the mantra “faster, better, cheaper”, where widespread adoption of independent low-cost solutions of onboard, in-stack sensors, combined with existing, globally ubiquitous satellite-based “automatic identification system” (AIS) data telemetry, provides an excellent solution to the affordable compliance and attribution conundrum for shipping companies and enforcement agencies alike. We present data from three field-campaigns which have significantly advanced the concept of onboard real-time monitoring of atmospheric ship emissions.

High level solution S4 of the IMO E-navigation Strategy Implementation Plan provides integration and presentation of available information in graphical displays received via communication equipment. At the same time, the problem of the correct application of digital selective calling (DSC) operational procedures in navigation practice has existed since the introduction of the GMDSS and requires a solution. This problem may be resolved on the base of integration maritime navigation and radiocommunication equipment. The article proposes approach for practical realization of this integration by implementing a SMART (specific, measurable, assignable, realistic, and timely) DSC interface within S4. Using this approach the practical realisation integration of AIS – DSC – Information display was implemented. It makes possible implementation of user-friendly human-machine interface (HMI) for navigator. An experimental prototype of communication graphical interface is designed, which allows effective decision-making on radio communication control/monitoring. The use IEC 61162-1/2 (Maritime navigation and radiocommunication equipment – Digital interfaces) data transfer provides standard inter-module connections and eliminates the negative impact of the equipment diversity from different manufacturers by means uniform HMI implementation.

The article discusses the principles of presenting data available from AIS and radar tracking on ship's radar display units and describes the results of comparative studies of the accuracy of their indications on sea-going vessels in real meeting situations in various hydro meteorological conditions.

Funding Information: The study described has been performed as part of the Detection, prediction, and solutions for safe operations of MASS (ENDURE) project (number NOR/POLNOR/ENDURE/0019/2019–00), supported by the Polish National centre for Research and Development and financed by Research Council of Norway. The authors are grateful to Danish Maritime Authority for making the AIS data available for analysis. Funding Information: The study described has been performed as part of the Detection, prediction, and solutions for safe operations of MASS (ENDURE) project (number NOR/POLNOR/ENDURE/0019/2019?00), supported by the Polish National centre for Research and Development and financed by Research Council of Norway. The authors are grateful to Danish Maritime Authority for making the AIS data available for analysis. Publisher Copyright: © 2021 The Author(s) Even in the era of automatization maritime safety constantly needs improvements. Regardless of the presence of crew members on board, both manned and autonomous ships should follow clear guidelines (no matter as bridge procedures or algorithms). To date, many safety indicators, especially in collision avoidance have been proposed. One of such parameters commonly used in day-to-day navigation but usually omitted by researchers is Bow Crossing Range (BCR). Therefore, this paper aims to investigate, what are typical, empirical values of BCR during routine operations of merchant ships, as well as investigate what factors impact this indicator and to what extent. To this end, a ten-year big dataset of real maritime traffic obtained from the Automatic Identification System (AIS) was used to provide statistical and spatiotemporal analyses. The results indicate that BCR is strongly related to the type of navigational area (open sea or restricted waters) but not with the dimensions or speed of ships. Among analyzed vessel types, passenger ships were noted as vessels that cross other bows at the closes ranges. Results of this study may be found interesting by fleet managers and developers of Maritime Autonomous Surface Ships (MASS). The former could utilize the results to provide revised operational guidelines for deck officers while the latter - propose an early-detection warning system based on empirical data for prospective MASS.

To mitigate marine atmospheric pollution causing greenhouse gas (GHG) and a threat to coastal residents’ health in dense hub port cities, the Vessel Speed Reduction (VSR) programs were implemented in the Republic of Korea. Spatial analysis of the efficiency of the VSR programs is essential to monitor and improve the present programs. In the present study, the efficiency of the VSRs from the Busan Port (BP), including North Port (NP) and Gamcheon Port (GP), were analyzed. A bottom-up activity-based approach using automatic identification system (AIS) data was introduced herein for the estimation of spatial marine gas emission in real time. The BP has implemented the VSR program since 2020; thus, this study spatially analyzed marine gas emissions in the areas in 2019 and 2020 to demonstrate the efficiency of VSR. To demonstrate the VSR programs in the aspect of the comparison of gas emissions in each year, the total annual fuel consumption in each year is divided by the total arrival ships’ GT respectively. According to the comparison of the spatial gas emission inventory between two years in the designed area, 19.2% of the annual marine gas emissions per ships’ GT in BP in 2020 were reduced with the implementation of the VSR program. The spatial analysis clearly showed the mitigation of the ships’ gas emissions along the passageway to BP. The spatial analysis of the efficiency of the VSR program will be useful basic data to reflect the local gas emission state on the improvement of the VSR program and newly established environmental regulations.

Previous researches on the prediction of fishing activities mainly rely on the speed over ground (SOG) as the referential attribute to determine whether the vessel is navigating or in fishing operation. Since more and more fishing vessels install Automatic Identification System (AIS) either voluntarily or under regulatory requirement, data collected from AIS in real time provide more attributes than SOG which may be utilized to improve the prediction. To be specific, the ships' trajectory patterns and the changes in course become available and should be considered. This paper aims to improve the accuracy in the identification of fishing activities. First, we do feature extraction from the AIS data of coastal waters around Taiwan and build a Recurrent Neural Network (RNN) model. Then, the activity data of fishing vessels are divided into fishing and non-fishing. Finally, based on the testing by feeding various fishing activity data, we can identify the fishing status automatically.

The Automatic Identification System (AIS) provides situational awareness for vessels at sea. AIS has a number of known security vulnerabilities that can lead to a several types of attacks on AIS, including the ability to create ghost vessels, false warning or meteorological messages, or bogus virtual aids-to-navigation (AtoN). A number of methods, with varying levels of complexity, have been proposed to better secure AIS and, indeed, emerging AIS protocols will implement some of these mechanisms. Nevertheless, little has been done to secure the current standards, which will remain in use for some time. This paper presents Protected AIS (pAIS), a demonstration of capability implementation using public-key cryptography methods to address several AIS security vulnerabilities, maintain backward compatibility, and be able to interoperate with non-pAIS devices.

The paper presents the method of determining ships traffic stream parameters by means of regression method. The aim of the studies was to determine the correlation between the ship's parameters and the parameters of the fairway. Developing the presented model with information on the position of the vessel's antenna and information on the accuracy of position determination will allow creating a model for predicting the parameters of waterways.

The operational measures in which a ship needs to avoid specified areas to escape ship stability failures were discussed at the International Maritime Organization as a part of the second generation intact stability criteria. It is necessary that the rationality and practicality of the operational measures are carefully investigated. In this study, master’s route decision-making criteria of trans-ocean vehicle carriers have been clarified by comparing the Pacific and the Atlantic data, derived from Satellite AIS and ocean wave data. Features of voyage routes of each ocean were discussed, followed by analysis of the encountered wave direction and height during a voyage. The master’s route selection criteria were defined by comparing the probability densities of the wave heights that occurred in the navigable area and that of the actual encountered waves. The navigation hours in a stormy area were also studied.

This research features an overview of the available PLB technologies, their advantages, shortcomings and areas of their optimum application. A test of the locator transmitter emitting both 406 MHz AIS and 121.5 MHz signals was performed with a focus on tracking the homing 121.5 MHz signal. The efficiency of the homing signal was examined by using two separate radio locating systems. One of them comprised multi-purpose and widely available components and programs, while the other was a specialised radio beacon system with dedicated components. In addition to the results, their analysis and evaluation of efficiency, the paper discusses the applicability of the available PLB technologies and provides guidelines for adequate selection of the PLB devices and position indicating radio equipment.

In the aftermath of the sinking of the US-flagged containership El Faro in October 2015, one of the U.S. National Transportation Safety Board’s (NTSB) recommendations was for the National Oceanic and Atmospheric Administration (NOAA) to explore increasing the collection of weather data from ships in order to improve the weather forecast products that they distribute. Currently NOAA runs the Voluntary Observing Ship (VOS) program, where ships voluntarily submit weather observations to NOAA. However, only a small fraction of the thousands of vessels sailing worldwide participate in this program, and VOS weather observations are submitted infrequently, typically four times per day via a mainly manual process. One method being explored to automate and increase the frequency of data submittal is by using the existing Automatic identification System (AIS) equipment installed aboard vessels. Most commercial ships (in particular those subject to the International Maritime Organization's (IMO) Safety of Life and Sea (SOLAS) convention) have a Class A AIS transceiver installed to comply with mandatory carriage requirements. Many vessels not required to carry AIS equipment voluntarily install AIS transceivers (either Class A or B). All of these AIS transceivers (except for the Class B “CS”) can be used to transmit an AIS message 8 (broadcast binary message) by sending the transceiver an appropriately formatted NMEA sentence (BBM). Weather data can be embedded in an AIS application-specific message (ASM) carried by the AIS message 8 and automatically transmitted by the ship. This transmission can be received by terrestrial AIS stations (when in range) or by satellite AIS receivers. The AIS weather data can then be converted into the appropriate format and forwarded to the weather forecasting offices for use in models and weather predictions. This data may also be of use to other researchers monitoring climate change or other environmental factors. By leveraging this existing base of AIS transmitters, the volume of weather data being sent to weather forecast offices and others could be greatly increased. The U.S. Army Corps of Engineers (USACE) and U.S. Maritime Administration (MARAD) have been exploring the feasibility of this concept. Following a simple bench test performed by Alion Science in September 2018, an initial proof-of-concept was tested aboard the MARAD vessel Cape Wrath while moored in Baltimore in October 2018. After this successful demonstration, a prototype was installed on the Massachusetts Maritime Academy training ship TS Kennedy during her training cruise Jan-Feb 2019. During this cruise, the AIS equipment aboard the ship transmitted weather data at 3-minute intervals. Several different ASM formats were tested, including two developed specifically for this test to improve satellite reception. This report will discuss the concept, the demonstrations, and the results to date including the efficacy of the various ASM formats.

A Yagi plasma antenna model was established by HFSS according to the relationship between plasma dielectric constant and electron density. The patterns were simulated by changing plasma parameters and the number of director dipoles. Results show that when the passive vibrators were switched off, the antenna is omnidirectional antenna. The directionality increases with the increase of the number of passive dipole and the main lobe of which narrows down. Then the plasma Yagi antenna model is established by plasma tube, the gain changed by changing the number of passive dipoles, so the plasma Yagi antenna has a very good reconfigurability. Results prove that the feasibility of the plasma Yagi antenna can be used on AIS base station of Ships’ Routeing waters. It can promote the communication and capability of maritime supervision in Ships’ Routeing waters.

To prevent pollution from ships, the Energy Efficiency Design Index (EEDI) is a mandatory guideline for all new ships. The Ship Energy Efficiency Management Plan (SEEMP) has also been applied by MARPOL to all existing ships. SEEMP provides the Energy Efficiency Operational Indicator (EEOI) for monitoring the operational efficiency of a ship. By monitoring the EEOI, the shipowner or operator can establish strategic plans, such as routing, hull cleaning, decommissioning, new building, etc. The key parameter in calculating EEOI is Fuel Oil Consumption (FOC). It can be measured on board while a ship is operating. This means that only the shipowner or operator can calculate the EEOI of their own ships. If the EEOI can be calculated without the actual FOC, however, then the other stakeholders, such as the shipbuilding company and Class, or others who don't have the measured FOC, can check how efficiently their ships are operating compared to other ships. In this study, we propose a method to estimate the EEOI without requiring the actual FOC. The Automatic Identification System (AIS) data, ship static data, and environment data that can be publicly obtained are used to calculate the EEOI. Since the public data are of large capacity, big data technologies, specifically Hadoop and Spark, are used. We verify the proposed method using actual data, and the result shows that the proposed method can estimate EEOI from public data without actual FOC.

The Automatic Identification System (AIS) offers automatic traffic control and collision avoidance services to the maritime transportation sector worldwide. Because AIS lacks security mechanisms, it is vulnerable to misuse and exploitation by unlawful adversaries (e.g. sea-pirates, terrorists, smugglers). To address the security issues of the AIS, in an earlier paper [1], we proposed the deployment of a Maritime Certificate-less Identity-Based (mIBC) public-key cryptography infrastructure that enhances AIS with on-demand anonymity, authentication, and encryption capabilities. In this paper we address implementation aspects of that infrastructure. In particular, we propose to use the Sakai-Kasahara Identity-Based Encryption (IBE) approach to implement the mIBC infrastructure, following the IEEE 1363.3-2013 standard for Identity-Based Cryptography.

With the aim to solve the problem of missing or tampering of ship type information in AIS information, in this paper, a novel ship type recognition scheme based on ship navigating trajectory and convolutional neural network (CNN) is proposed. Firstly, according to speed and acceleration of the ship, three ship navigating situations, i.e., static, normal navigation and maneuvering, are integrated into the process of trajectory images generation in the form of pixels. Then, three kinds of modular network structures with different depths are trained and optimized to determine the appropriate convolutional neural network structure. In the validation phase of the model, a large amount of verified data with a time span of one month was used, covering a variety of water conditions including open water, ports, rivers and lakes. Following this approach, a kind of CNN scheme which can be directly used to identify ship types in a wide range of waters is proposed. This scheme can be used to judge the ship type when the static information is completely missing and to test the data when the ship type information is partially missing.

This thesis presents the study and analysis of the Vessel Traffic Services (VTS), from its origins to the present, with further evaluation of future trends. Examining the current trend in terms of technologies that are being applied in the maritime sector, the dissertation focuses on the future to draw a new paradigm. This envisages a scenario at a planetary level, following a single global traffic management system, which would create a model of the global VTS Operator. A new assignment is proposed, according to this third dimension, with Port VTS being the first level, the Coastal VTS second, and with the Oceanic VTS projected into the future, which is a substantial proposal. The foundation is based on EU projects, the most ambitious of which, currently underway, being Sea Traffic Management (STM), with a scope greater than Europe, and also on IALA VTS and ENAV Committee Working Groups, concerning the future of VTS. The study includes the reference documentation from the IMO, in resolution format, and that of the IALA, under Standards, Recommendations and Guidelines. From the first recognition of the IMO to the present, the guidelines for VTS have undergone three updates, which are analysed and compared. VTS are considered aids to navigation, AtoN, which contribute to safety of life at sea, safety and efficiency of navigation and protection of the marine environment, and are linked to ship traffic activity. Both their attributions and the implementation of VTS centres have increased over time. In addition to this, the evolution of the tools used by the VTS is examined, from its earliest stages with only two pieces of equipment, the VHF radio, and the radar. The VTS system began by looking at the air traffic model but, later, the maritime nature created necessary differentiation in terms of management and procedures. Digital technologies applied to the maritime sector take air traffic controllers’ models as a reference, and the current VTS Operator profession will acquire analogies, such as sharing route plans and checking digital inputs. This highly specialised profession is facing the challenges of satellite technology, global interconnection, and the arrival of Maritime Autonomous Surface Ships (MASS). New technologies bring the possibility of sharing information and controlling traffic on a global scale, beyond the territorial sea, and this scenario will make standardisation increasingly necessary throughout the maritime sector. The identification of various models of traffic management, examination of systems operating specifically in the Spanish State and reflection on the diversity that prevails is also included. Based on fieldwork, the thesis determines that the future complexities that will emerge from maritime traffic will require exhaustive risk assessment and analysis. The challenges to be faced involve maritime communications via satellite and digital technology, in the context of ‘e-Navigation’ (coined by the IMO), which will provide emerging tools, such as VDES (next generation of AIS), and a planetary-scale information exchange framework for VTS worldwide. Aquesta tesi presenta l'estudi i l'anàlisi dels Serveis de Tràfic Marítim (VTS) des dels seus orígens fins al present, avaluant tendències futures. Examinant el corrent actual en referència a les tecnologies que s'estan aplicant al sector marítim, la tesi s’orienta al futur per delinear un nou paradigma. Un escenari a nivell planetari, que segueix un únic sistema de gestió de tràfic global, i que crearà un model d'operador VTS global. Es proposa una nova assignació acord a aquesta tercera dimensió, sent el VTS Port, el primer, el VTS Costaner, el segon, i es projecta a futur el VTS Oceànic, que és la proposta substancial. La fundació es basa en projectes de la UE, on és el més ambiciós i actualment en marxa el Sea Traffic Management (STM), amb un abast superior a l'europeu i amb els grups de treball del Comitè VTS i ENAV de la IALA sobre l’esdevenidor del VTS. L’estudi inclou documentació de referència procedent de la IMO, en format resolucions; i la procedent de la IALA, sota estàndards, recomanacions i directrius. Des del primer reconeixement de la IMO fins ara, les directrius per als VTS han passat per tres actualitzacions, que s'analitzen i es comparen. Els VTS es consideren ajuts a la navegació, AtoN, que contribueixen a la seguretat de la vida humana al mar, la seguretat i l'eficiència de la navegació i la protecció del medi marí i estan vinculats a l'activitat del trànsit de vaixells, i tant les seves atribucions com la implantació de centres VTS han augmentat al llarg de temps. A més a més d'això, s'examina l'evolució de les eines utilitzades pel VTS, des de les seves primeres etapes amb només dos equips, la ràdio VHF i el radar. El sistema VTS es va iniciar fixant-se en el model aeri, però posteriorment la naturalesa marítima els va diferenciar pel que fa a gestió i procediments. Les tecnologies digitals aplicades al sector marítim prenen com a referència models similars als controladors aeris, i l'actual professió d'Operador VTS assolirà analogies com ara compartir plans de ruta amb els vaixells i comprovació d'inputs digitals. Una professió altament especialitzada que s'enfronta als reptes de la tecnologia satel·litària, la interconnexió mundial i l'arribada dels vaixells marítims autònoms de superfície (MASS). Les noves tecnologies porten la possibilitat de compartir informació i controlar el tràfic a escala global, més enllà del mar territorial, i aquest escenari farà que l'estandardització sigui cada cop més necessària a tot el sector marítim. Es considera la identificació de diversos models de gestió del tràfic, els sistemes que operen específicament a l'Estat espanyol i la reflexió sobre la diversitat que preval. A partir d'un treball de camp, la tesi determina que les complexitats que emergiran del tràfic marítim requeriran avaluació i anàlisi de riscos exhaustius. Els reptes que cal afrontar són aquells que inclouen les comunicacions marítimes via satèl·lit i la tecnologia digital en el context de l'e-Navegació, encunyat per la IMO, que proporcionaran eines emergents, com els VDES (propera generació de l’AIS), i un marc d’intercanvi d’informació a escala planetària per als VTS de tot el món. Enginyeria nàutica, marina i radioelectrònica naval

Maritime activity is expected to increase, and therefore also the need for maritime surveillance and safety. Most ships are obligated to identify themselves with a transponder system like the Automatic Identification System (AIS) and ships that do not, intentionally or unintentionally, are referred to as dark ships and must be observed by other means. Knowing the future location of ships can not only help with ship/ship collision avoidance, but also with determining the identity of these dark ships found in, e.g., satellite images. However, predicting the future location of ships is inherently probabilistic and the variety of possible routes is almost limitless. We therefore introduce a Bidirectional Long-Short-Term-Memory Mixture Density Network (BLSTM-MDN) deep learning model capable of characterising the underlying distribution of ship trajectories. It is consequently possible to predict a probabilistic future location as opposed to a deterministic location. AIS data from 3631 different cargo ships are acquired from a region west of Norway spanning 320,000 sqkm. Our implemented BLSTM-MDN model characterizes the conditional probability of the target, conditioned on an input trajectory using an 11-dimensional Gaussian distribution and by inferring a single target from the distribution, we can predict several probable trajectories from the same input trajectory with a test Negative Log Likelihood loss of −9.96 corresponding to a mean distance error of 2.53 km 50 min into the future. We compare our model to both a standard BLSTM and a state-of-the-art multi-headed self-attention BLSTM model and the BLSTM-MDN performs similarly to the two deterministic deep learning models on straight trajectories, but produced better results in complex scenarios.

The Automatic Identification System (AIS) uses Very High Frequency (VHF) to communicate navigational information among vessels and between vessels and shore. This work presents recent research on the application of advanced digital technologies for additional utilization of AIS data in support of navigation, including microservices architecture-based platform for providing maritime data, a novel data compression method for use in transmission of large quantities of shipboard data required in future autonomous vessel operation and the possibilities of the application of some more advanced computational methods, such as machine learning, in AIS data analysis.

This paper provides an overview of cyber threats and actual incidents in the maritime sector, identifying nowadays challenges related to safe navigation. The vulnerabilities of satellite navigation systems, particularly the Global Positioning System (GPS) and the Automatic Identification System (AIS), receive special attention. For this article, a systematic literature review was conducted, complemented by an analysis of recent cyber-attacks and their influence on safe navigation. The analysis show vulnerabilities of maritime navigational systems against cyber threats and generation of erroneous position information. The maritime industry is neither immune to cyberattacks nor fully prepared for the risks associated with the use of modern digital systems. Maintaining seaworthiness in the face of the impact of digital technologies requires a shift in mindset to direct more attention and resources toward cybersecurity and a robust cybersecurity framework.

The automatic identification system (AIS), despite its importance in worldwide navigation at sea, does not provide any defence mechanisms against deliberate misuse, e.g., by sea pirates, terrorists, business adversaries, or smugglers. Previous work has proposed an international maritime identity-based cryptographic infrastructure (mIBC) as the foundation upon which the offer of advanced security capabilities for the conventional AIS can be built. The proposed secure AIS (SecAIS) does not require any modifications to the existing AIS infrastructure, which can still be used for normal operations. Security-enhanced AIS messages enjoying source authentication, encryption, and legitimate pseudo-anonymization can be handled on an as-needed basis. This paper reports on a proof-of-concept implementation of the SecAIS. Specifically, we report on the implementation of the SecAIS over an mIBC founded on the RFC6507 (ECCSI) and the RFC6508 (SAKKE) standards, and we discuss the results of performance tests with this implementation. The tests indicate that the SecAIS is a feasible solution that does not affect the conventional AIS infrastructure and has an affordable operational cost.

Dense hub port-cities have been suffering from ship gas emissions causing atmospheric pollution and a threat to the health of coastal residents. To control ship gas emissions, many regulations have been established internationally. Analyses of ship gas emission inventories are essential to quantify mass and track emission changes over time in a given geographical area. Based on the gas emissions inventory, applicable regulations such as Emission Control Area (ECA) realization and Vessel Speed Reduction (VSR) may be established. The ship gas emission inventory (CO2, CO, NOx, SOx and PM) from the Busan Port (BP), including the North Port (NP) and Gamcheon Dadae-po Port (GDP), which is the biggest port in the Republic of Korea and which is also surrounded by residential, commercial, and industrial areas, were spatially analyzed. To calculate geographical ship gas emissions in real-time, this study introduces a bottom-up methodology using Automatic Identification System (AIS) data. According to the geographical density analysis of the gas emissions inventory, this study highlights that about 35% of the annual ship gas emissions of BP in 2019 were concentrated in the passageway to NP because of high ship speeds when leaving or arriving at the port. To protect the health of coastal residents, ship speed limit regulations along the passageway should be revised based on our spatial analysis results. The spatial analysis of the ship gas emission inventory in BP will be useful basic data for properly evaluating the local gas emission state on newly established or revised environmental regulations for BP.

With the continuous development of earth observation technology, space-based synthetic aperture radar (SAR) has become an important source of information for maritime surveillance, and ship classification in SAR images has also become a hot research direction in the field of maritime ship monitoring. In recent years, the remote sensing community has proposed several solutions to the problem of ship object classification in SAR images. However, it is difficult to obtain an adequate amount of labeled SAR samples for training classifiers, which limits the application of machine learning, particularly deep learning methods, in SAR image ship object classification. In contrast, as a real-time automatic tracking system for monitoring ships at sea, a ship automatic identification system (AIS) can provide a large amount of relatively easy-to-obtain labeled ship samples. Therefore, to solve the problem of SAR image ship classification and improve the classification performance of learning models with limited samples, we proposed a SAR image ship classification method based on multiple classifiers ensemble learning (MCEL) and AIS data transfer learning. The core idea of our method is to transfer the MCEL model trained on AIS data to SAR image ship classification, which mainly includes three steps: first, we use the acquired global space-based AIS data to build a dataset for ship object classification models training; then, the ensemble learning model is constructed by combining multiple base classifiers; and finally, the trained classification model is transferred to SAR images for ship type prediction. Experiments show that the proposed method achieves a classification accuracy of 85.00% for the SAR ship classification, which is better than the performance of each base classifier. This proves that AIS data transfer learning can effectively solve the problem of SAR ship classification with limited samples, and has important application value in maritime surveillance.

Defining the appropriate functional requirements in the early ship design stage is important in order that costs that are caused by the over- or under-specified functional capabilities do not increase. This paper presents a K-means clustering algorithm for the determination of functional requirements. It uses automatic identification system (AIS) data from a reference ship to determine the representative operational profiles, which can support decision-makers in defining the functional requirements of ships that will be performing similar missions as those of the reference ship. In a case study, we used this method as part of a ship design project, in which the functional requirements of a battery-only electric ship are defined using AIS data from a reference ship. Results indicate that the cost can be reduced by determining the functional requirements using the proposed method.

The efficiency and safety of maritime traffic in a given area can be measured by analyzing traffic density and ship collision probability. Maritime traffic density is the number of ships passing through a given area in a given period of time. It can be measured using vessel tracking systems, such as the Automatic Identification System (AIS). The information provided by AIS is real-time data designed to improve maritime safety. However, the AIS data can also be used for scientific research purposes to improve maritime safety by developing predictive models for collisions in a research area. This article proposes a ship collision probability estimation model based on Monte Carlo simulation (MC) and bidirectional long short-term memory neural network (Bi-LSTM) for the maritime region of Split. The proposed model includes the processing of AIS data, the verification of AIS data, the determination of ports and ship routes, MC and the collision probability, the Bi-LSTM learning process based on MC, the ship collision probability for new or existing routes, and the traffic density. The results of MC, i.e., traffic/vessel route and density, and collision probability for the study area can be used for Bi-LSTM training with the aim of estimating ship collision probability. This article presents the first part of research that includes MC in detail, followed by a preliminary result based on one day of processed AIS data used to simulate MC and propose a model architecture that implements Bi-LSTM for ship collision probability estimation.

This paper takes a close look at the landscape of the Automatic Identification System (AIS) as a major source of information for maritime situational awareness (MSA) and identifies its vulnerabilities and challenges for safe navigation and shipping. As an important subset of cyber threats affecting many maritime systems, the AIS is subject to problems of tampering and reliability, indeed, the messages received may be inadvertently false, jammed, or intentionally spoofed. A systematic literature review was conducted for this article, complemented by a case study of a specific spoofing event near Elba in December 2019, which confirmed that the typical maritime AIS could be easily spoofed and generate erroneous position information. This intentional spoofing has affected navigation in international waters and passage through territorial waters. The maritime industry is neither immune to cyberattacks nor fully prepared for the risks associated with the use of modern digital systems. Maintaining seaworthiness in the face of the impact of digital technologies requires a robust cybersecurity framework.

In many coastal areas, high numbers of recreationists may exceed ecological capacities. Careful monitoring of visitor flows is a first prerequisite for coastal area management. We show how AIS ship data can be translated into interpretable information on recreational boats and investigate whether AIS can provide monitoring information when compared to nature conservation policy targets. In the Wadden Sea UNESCO World Heritage Site we used nearly 9 million data points to create spatiotemporal patterns for the 2018 recreation season. We combined this with shipping lanes and bathymetry data and compared the resulting patterns with nature protection regulations. Our results show that most of the traffic is concentrated around tidal channels. We also show that exceeding speed limits is not predominant behaviour, but the effect of speeding on birds and seals might be more severe than the data suggests. We mapped favourite tidal flat moor activities, and observed where this occurs in Marine Protected Areas. We conclude that AIS analysis can provide valuable recreational boating monitoring, relevant to sensitive coastal area management in the entire Dutch Wadden Sea for the full recreational season. Broader integration of AIS with radar data and ecological data can add to the power of using AIS.

The currently existing Biological Protection Areas (BPAs) are Italian conservation measures specifically oriented to preserve/recover commercial fish stocks through the regulation or ban of few fishing activities. Although BPAs have been well identified/designed within the Italian national waters, neither data on their effects on fishery resources nor on the fishers’ compliance with these no-take regulations are yet available. In this context, the present study was aimed to investigate how AIS data processing could be used to map patterns in fishing activity within/around small regulated areas and rate the effectiveness of the conservation measures in place. To do this, it was impossible not to address the issue surrounding well-known and substantial gaps in data coverage and attempt an estimate of them by flagging events when AIS had been switched off within/around BPAs. Results highlighted that almost all the BPAs are illegally trawled and that, unless additional legislation is effected to regulate the use of AIS by fishing vessels, it may provide a useful source of information to map fishing activities stated that the data are interpreted in an appropriate way.

To use the existing Automatic Identification System (AIS) shore stations for positioning so that the AIS can be used as an additional land-based positioning system for coastal vessels is a cutting-edge research topic, responding to the call of the International Maritime Organization (IMO). In order to use the ship-borne AIS for positioning function, a holographic detection of AIS real-time signal based on sparse representation is presented in this paper. Considering the working environment and the requirement of AIS real-time signal processing, a novel fast noise resistance Orthogonal Matching Pursuit (OMP) algorithm is presented. Furthermore, the choice and detection of the timestamp of the reconstructed signal is analyzed and carried out which will be used in the ranging system. The experiment results indicate that the proposed fast noise resistance OMP algorithm can greatly reduce the processing time, and the difference in processing time increases with the number of iterations. The improvement in noise immunity is also obvious, and the error rate reduces at about 9% under the same SNR. The timestamp of the reconstructed signals can be detected successfully. It shows that the holographic detection of AIS real-time signal is achieved satisfactorily.

Optimal ship tracks computed via the VISIR model are compared to tracks recorded by the Automatic Identification System (AIS). The evaluation regards 43 tracks in the Southern Atlantic Ocean, sailed during 2016-2017 by different bulk carriers. In this exercise, VISIR is fed by wave analysis fields from the Copernicus Marine Environment Monitoring Service (CMEMS). In order to reproduce vessel speed loss in waves, a new methodology is developed, where kinematic information from AIS is fusioned with wave information from CMEMS. Resulting VISIR tracks are analyzed along with AIS tracks in terms of their topological features and duration. The tracks exhibit quite diverse topological shapes, including orthodromic, loxodromic, and other paths with complex and dynamic diversions. The distribution of AIS to VISIR track durations is analyzed in terms of several parameters, such as the AIS to VISIR track length and their Fréchet distance. Model features of VISIR affecting the results are discussed and future developments suggested by the results are outlined.

Ship route planning is one of the key issues in enhancing traffic safety and efficiency. Many route planning methods have been developed, but most of them are based on the information from charts. This paper proposes a method to generate shipping routes based on historical ship tracks. The ship's historical route information was obtained by processing the AIS data. From which the ship turning point was extracted and clustered as nodes. The ant colony algorithm was used to generate the optimize route. The ship AIS data of the Three Gorges dam area was selected as a case study. The ships’ optimized route was generated, and further compared with the actual ship's navigation trajectory. The results indicate that there is space of improvement for some of the trajectories, especially near the turning areas.

Digital selective calling (DSC) has a certain drawbacks, which have led to the fact that it is practically not used in address radio communications. Attempts to improve it only within the DSC system itself did not lead to any noticeable results due to neglecting of real user needs. The article proposes an approach to improve the user interface of DSC based on the integration of VHF MF/HF DSC equipment with the INS within the three main outputs of strategic direction of e-navigation development related to the integration of communication and navigation systems and, in particular, the S-mode. This approach allows us to significantly simplify the DSC user interface, based on the standardization of DSC integration for all three VHF MF/HF communication sub bands. For implementation such standardization an additional inclusion into the integration a satellite AIS system should be done. The feasibility of the proposal is supported by the existence of relevant IEC standards and IMO documents. The possibility of increasing the cyber security level of AIS messages in this interface is also considered.

This paper proposes an approach for identifying and characterizing shipping routes using information contained in Automatic Identification System messages broadcasted by ships and recorded by the coastal Vessel Traffic Service centre. The approach consists of using historical Automatic Identification System data to build a graph, where nodes are cells of a grid covering the geographical area being studied and the weights of directional edges are inversely related to ship movements between cells. Based on this graph, the Dijkstra algorithm is used to identify a potential safe route, assumed to be the most used route by ships between two locations. A second graph is created simultaneously, with the same nodes and edges, but with edge weights equal to the average speed of transitions between cells, thus allowing the determination of the average speed profile for any possible path within the graph. The proposed approach is applied to two scenarios: an approach to the port of Lisbon and the entry through the fairway to a RO-RO terminal in the port of Setubal in Portugal.

Ship navigation and operational activities at sea need meteorological and hydrographic information to enhance safety and efficiency. Besides observations collected from deployed data buoys, marine meteorological information services rely on weather observation reports from ships to increase the data density and thus service quality. Automatic Identification System (AIS), adopted internationally to facilitate ship-ship and ship-shore data exchange, has developed into communication links between ship/shore and buoys with many potential applications. This paper presents AIS applications designed and implemented for marine meteorological information services in a long-term project initiated by the Central Weather Bureau of Taiwan. Achievements of this initiative include: versatile service delivery via shore-based AIS network to shipborne application platforms such as smart phones, remote controllable moving weather data collection, and assisted sharing of weather observation reports from ships. Typhoon forecast and warning is one of the key applications implemented for this region.

The article presents analysis of the automatic radar plotting aid (ARPA) and automatic identification system (AIS) indications reliability performed on the base of the results of measurements conducted on merchant vessels at sea. In the first part of the article titled “Comparative study of the accuracy of AIS and ARPA indications. Part 1. Accuracy of the CPA indications” are described: vessels on which the tests were carried out, AIS and radar equipment installed on them, observed meeting situations and accuracy of the CPA indication. In this article are discussed, for the same meeting situations, accuracy of the information on true course and true speed of the opposite vessel presented by ARPA and AIS and correlation between this accuracies and errors of the CPA indication.

The COVID-19 pandemic has resulted in unparalleled global impacts on human mobility. In the ocean, ship-based activities are thought to have decreased due to severe restrictions and changes in goods consumption, but little is known of the patterns of change, which sectors are most affected, in which regions, and for how long. Here, we map global change of marine traffic during the COVID-19 pandemic and assess its temporal variability at a fine-scale in one of the most affected regions, the Mediterranean Sea. Nearly 44.3% of the global ocean and 77.5% of national jurisdictions showed a decrease in traffic density during April 2020, when strictest confinement measures took place, showing a clear disruption in comparison with previous trends and future projections. Decreases mainly occurred in coastal areas and were more marked and longer lasting in sectors other than cargo and tanker shipping. Our results provide guidance for large-scale monitoring of the progress and potential effects of COVID-19, or other global shocks, on the blue economy and ocean health., We thank Marine Traffic and Exact Earth for their support with AIS data. D.M. acknowledges support from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska‐Curie grant agreement no 794938

openaire: EC/H2020/|723526/EU//SEDNA Ships operating in ice-infested Arctic waters are exposed to a range of ship-ice interaction related hazards. One of the most dangerous of these is the possibility of a ship becoming beset in ice, meaning that a ship is surrounded by ice preventing it from maneuvering under its own power. Such a besetting event may not only result in severe operational disruption, but also expose a ship to severe ice loading or cause it to drift towards shallow water. This may cause significant structural damage to a ship and potentially jeopardize its safety. To support safe and sustainable Arctic shipping operations, this article presents a probabilistic approach to assess the probability of a ship becoming beset in ice. To this end, the proposed approach combines different types of data, including Automatic Identification System (AIS) data, satellite ice data, as well as data on real-life ship besetting events. Based on this data, using a hierarchical Bayesian model, the proposed approach calculates the probability of a besetting event as a function of the Polar Ship Category of a ship, sea area, and the distance travelled in the prevailing ice concentration. The utility of the proposed approach, e.g. in supporting spatiotemporal risk assessments of Arctic shipping activities as well as Arctic voyage planning, is demonstrated through a case study in which the approach is applied to ships operating in the Northern Sea Route (NSR) area. The outcomes of the case study indicate that the probability of besetting is strongly dependent on the Polar Ship Category of a ship and that the probability increases significantly with higher ice concentrations. The sea area, on the other hand, does not appear to significantly affect the probability of besetting.

Spatially explicit records of fishing activities’ distribution are fundamental for effective marine spatial planning (MSP) because they can help to identify principal fishing areas. However, in numerous case studies, MSP has ignored fishing activities due to data scarcity. The vessel monitoring system (VMS) and the automatic identification system (AIS) are two commonly known technologies used to observe fishing activities. However, both technologies generate data that have several limitations, making them ineffective when used in isolation. Here, we evaluate both datasets’ limitations and strengths, measure the drawbacks of using any single dataset and propose a method for combining both technologies for a more precise estimation of the distribution of fishing activities. Using the Baltic Sea and the North Sea–Celtic Sea regions as case studies, we compare the spatial distribution of fishing effort from International Council for the Exploration of the Seas (ICES) VMS data and global fishing watch AIS data. We show that using either dataset in isolation can lead to a significant underestimation of fishing effort. We also demonstrate that integrating both datasets in an ensemble approach can provide more accurate fisheries information for MSP. Given the rapid expansion of MSP activities globally, our approach can be utilised in data-limited regions to improve cross border spatial planning.

Collision avoidance is one of the main tasks on board ships to ensure safety at sea. To comply with this requirement, the direct ship environment, which is often modelled as the ship’s domain, has to be kept free of other vessels and objects. This paper addresses the question to which extent inaccuracies in position (P), navigation (N), and timing (T) data impact the reliability of collision avoidance. Employing a simplified model of the ship domain, the determined error bounds are used to derive requirements for ship-side PNT data provision. For this purpose, vessel traffic data obtained in the western Baltic Sea based on the automatic identification system (AIS) is analysed to extract all close encounters between ships considered as real-world traffic situations with a potential risk of collision. This study assumes that in these situations, erroneous data can lead to an incorrect assessment of the situation with regard to existing collision risks. The size of the error determines whether collisions are detected, spatially incorrectly assigned, or not detected. Therefore, the non-recognition of collision risks ultimately determines the limits of tolerable errors in the PNT data. The results indicate that under certain conditions, the probability of non-recognition of existing collision risks can reach non-negligible values, e.g., more than 1%, even though position accuracies are better than 10 m.

Data-driven methods open up unprecedented possibilities for maritime surveillance using Automatic Identification System (AIS) data. In this work, we explore deep learning strategies using historical AIS observations to address the problem of predicting future vessel trajectories with a prediction horizon of several hours. We propose novel sequence-to-sequence vessel trajectory prediction models based on encoder-decoder recurrent neural networks (RNNs) that are trained on historical trajectory data to predict future trajectory samples given previous observations. The proposed architecture combines Long Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data and generate future predictions with different intermediate aggregation layers to capture space-time dependencies in sequential data. Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority show the effectiveness of deep-learning methods for trajectory prediction based on sequence-to-sequence neural networks, which achieve better performance than baseline approaches based on linear regression or on the Multi-Layer Perceptron (MLP) architecture. The comparative evaluation of results shows: i) the superiority of attention pooling over static pooling for the specific application, and ii) the remarkable performance improvement that can be obtained with labeled trajectories, i.e., when predictions are conditioned on a low-level context representation encoded from the sequence of past observations, as well as on additional inputs (e.g., port of departure or arrival) about the vessel's high-level intention, which may be available from AIS., Accepted for publications in IEEE Transactions on Aerospace and Electronic Systems, 17 pages, 9 figures

Su ürünleri üretimi ve deniz taşımacılığı ülkemiz ve dünya ekonomisi için çok önemli faaliyetlerdir. İzmir Körfezi'nde de yoğun bir şekilde gerçekleşen bu iki faaliyet, balıkçı gemileri ve ticari gemilerin karşılaştığı zamanlarda seyir emniyeti açısından incelenmiştir. Ticari gemiler, körfez içerisinde otomatik tanımlama sistemi (AIS) bulunmayan ufak boylu balıkçı teknelerinin seyir emniyetlerini riske attığı durumunu raporlamaktadırlar. İzmir Gemi Trafik Hizmetleri kayıtları incelendiğinde balıkçı gemilerinin ticari gemilerin seyir güvenliğini riske attığı durumu tüm deniz olayları içerisinde; 2019 yılı için %66, 2020 yılı için %67, 2021 yılının ilk dört ayı için %75 olduğu belirlenmiştir. Otomatik tanımlama sistemi (AIS) bulundurma zorunluluğu için belirlenen boy sınırının altında olan, izlenemeyen balıkçı gemi ve teknelerinin seyir emniyetine olumsuz etki ettiği durumu, gemi izleme sistemi ile incelenmiştir. Yapılan çalışmalarda ülkemiz suları ve İzmir Körfezi için görülen eksiklikler ve önerilerin geçen süre boyunca nasıl seyir gösterdiği ve mevcut durum incelenmiştir. Eksiklikler sebebiyle meydana gelebilecek can, mal, itibar kaybını önlemek amacıyla gerekli önerilerde bulunulmuştur., Fisheries production and maritime transportation are crucial activities for the economy of our country and the world. These two activities which also take place in a very busy way in the gulf of Izmir are examined in terms of fishing vessels and navigational safety when merchant ships come across. Merchant ships report that small-scale fishing boats that do not have Automatic Identification System (AIS) within the gulf, risk their navigational safety. When Izmir Vessel Traffic Services go into the records, it is stated that this risk against navigational safety of merchant ships by fishing boats was 66% in 2019; 67% in 2020, and 75% in the first four months of 2021. The fact that fishing vessels and boats that are not monitored and under the limit of the length determined for the necessity of Automatic Identification System (AIS) have a negative effect on navigational safety has been examined with vessel monitoring system. Previous studies analyze the current situation and how deficiencies and suggestions about the territorial water of Turkey and the gulf of Izmir have progressed. The necessary suggestions have been made in order to prevent any loss of life, property and reputation due to the deficiencies.

The widespread of shipborne Automatic Identification System (AIS) equipment will continue to produce a large amount of spatiotemporal trajectory data. In order to explore and understand the hidden behaviour patterns in the data, an interactive visual analysis method combining multiple views is proposed. The method mainly includes four parts: using a trajectory compression algorithm that takes into account the vessel motion characteristics to preprocess the vessel trajectory data; displaying and replaying vessel trajectories based on Electronic Chart System (ECS), and proposing a detection algorithm for vessel stay points based on the principle of spatiotemporal density to semantically label vessel trajectories; using the Fast Dynamic Time Warping (FastDTW) similarity measurement algorithm and the Ordering Points to Identify the Clustering Structure (OPTICS) clustering algorithm to cluster vessel trajectories to show the differences and similarities between vessel traffic flows; and showing the distribution of vessels and the variation trend of vessel density based on the vessel heatmap. Based on the AIS data of the Yangtze River Estuary, three cases are used to prove the usefulness and effectiveness of the system in vessel behaviour analysis.