Automatic Identification System Trajectory Research Articles

A method for compressing AIS trajectory based on the adaptive core threshold difference Douglas–Peucker algorithm

Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas–Peucker (DP) algorithm, typically use static thresholds based on fixed parameters like ship dimensions or predetermined distances, which limits their adaptive capabilities. In this paper, the adaptive core threshold difference-DP (ACTD-DP) algorithm is proposed based on traditional DP algorithm. Firstly, according to the course value of automatic identification system (AIS) data, the original trajectory data is preprocessed and some redundant points are discarded. Then the number of compressed trajectory points corresponding to different thresholds is quantified. The function relationship between them is established by curve fitting method. The characteristics of the function curve are analyzed, and the core threshold and core threshold difference are solved. Finally, the compression factor is introduced to determine the optimal core threshold difference, which is the key parameter to control the accuracy and efficiency of the algorithm. Five different algorithms are used to compress the all ship trajectories in the experimental water area. The average compression ratio (ACR) of the ACTD-DP algorithm is 87.53%, the average length loss ratio (ALLR) is 23.20%, the AMSED (mean synchronous Euclidean distance of all trajectories) is 68.9747 mx, and the TIME is 25.6869 s. Compared with the other four algorithms, the ACTD-DP algorithm shows that the algorithm can not only achieve high compression ratio, but also maintain the integrity of trajectory shape. At the same time, the compression results of four different trajectories show that ACTD-DP algorithm has good robustness and applicability. Therefore, ACTD-DP algorithm has the best compression effect.

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

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

A Method for Clustering and Analyzing Vessel Sailing Routes Efficiently from AIS Data Using Traffic Density Images

A vessel automatic identification system (AIS) provides a large amount of dynamic vessel information over a large coverage area and data volume. The AIS data are a typical type of big geo-data with high dimensionality, large noise, heterogeneous densities, and complex distributions. This poses a challenge for the clustering and analysis of vessel sailing routes. This study proposes an efficient vessel sailing route clustering and analysis method based on AIS data that uses traffic density images to transform the clustering problem of complex AIS trajectories into an image processing problem. First, a traffic density image is constructed based on the statistics of the preprocessed AIS data. Next, the main sea route regions of traffic density images are extracted based on local image features, geometric structures, and spatial features. Finally, the sailing trajectories are clustered using the extracted sailing patterns. Based on actual vessel AIS data, multimethod comparisons and performance analysis experiments are conducted to verify the feasibility and effectiveness of the proposed method. These experimental results reveal that the proposed method displays potential for the clustering task of challenging vessel sailing routes.

Navigation pattern extraction from AIS trajectory big data via topic model

AbstractThis paper introduces a novel approach for extracting vessel navigation patterns from very large automatic identification system (AIS) trajectory big data. AIS trajectory data records are first converted to a series of code documents using vector quantisation, such as k-means and PQk-means algorithms, whose performance is evaluated in terms of precision and computational time. Therefore, a topic model is applied to these code documents from which vessels’ navigation patterns are extracted and identified. The potential of the proposed approach is illustrated by several experiments conducted with a practical AIS dataset in a region of North West France. These experimental results show that the proposed approach is highly appropriate for mining AIS trajectory big data and outperforms common DBSCAN algorithms and Gaussian mixture models.

WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory

The Automatic Identification System (AIS) has recorded near-real-time vessel monitoring data over the years, paving the way for data-driven maritime surveillance methods; concurrently, the data suffer from unrefined, reliability issues and irregular intervals. In this paper, we address the problem of vessel destination estimation by exploiting the global-scope AIS data. We propose a differentiated data-driven approach recasting a long sequence of port-to-port international vessel trajectories as a nested sequence structure. Based on spatial grids, this approach mitigates the spatio-temporal bias of AIS data while preserving the detailed resolution of the original. Further, we propose a novel deep learning architecture (WAY) that is designed to effectively process the reformulated trajectory and perform the long-term estimation of the vessel destination ahead of arrival with a horizon of days to weeks. WAY comprises a trajectory representation layer and channel-aggregative sequential processing (CASP) blocks. The representation layer produces the multi-channel vector sequence output based on each kinematic and non-kinematic feature collected from AIS data. Then CASP blocks include multi-headed channel- and self-attention architectures, where each processes aggregation and sequential information delivery respectively. Then, a task-specialized learning technique, Gradient Dropout (GD), is also suggested for adopting many-to-many training along the trajectory progression on single labels. The technique prevents a surge of biased feedback by blocking the gradient flow stochastically using the condition depending on the length of training samples. Experimental results on 5-year accumulated AIS data demonstrated the superiority of WAY with recasting AIS trajectory compared to conventional spatial grid-based approaches, regardless of the trajectory progression steps. Moreover, the data proved that adopting GD in a spatial grid-based approach leads to the performance gain. In addition, the possibilities of improvement and real-world application with WAY's expandability in multitask learning for the estimation of ETA was explored.

Compressing AIS Trajectory Data Based on the Multi-Objective Peak Douglas–Peucker Algorithm

The automatic identification system (AIS) provides a massive database for ocean science. The original AIS data are redundant. Direct use will cause a waste of data storage space and computation costs; hence, data compression must be performed. The Douglas–Peucker algorithm (DP) is an effective trajectory compression algorithm that can well preserve the spatial characteristics of a trajectory but has the following shortcomings: first, it has poor track recovery when compressing multi-turn routes; second, it does not consider the ship speed and heading; and third, it may have the wrong result of the compressed trajectory crossing the obstacle. To address these situations, this study proposes a multi-objective peak DP algorithm (MPDP) that adopts a peak sampling strategy, considers three optimization objectives (spatial characteristics, heading and speed) of trajectory and adds an obstacle detection mechanism to realize a compression algorithm more suitable for curved trajectories. The classical DP algorithm is compared with the MPDP algorithm by simulating trajectory and real trajectory experiments. The results show that the MPDP algorithm optimizes the length loss rate, simultaneous Euclidean distance, and average deviations of the speed and the heading while maintaining a high compression rate similar to that of the DP algorithm. Moreover, it can also successfully avoid obstacles. The optimization effect is most obvious for the multi-turn or hovering trajectory. The optimization rate of length loss, synchronous Euclidean distance, and average deviation of the heading can reach 40%.

Ship carbon dioxide emission estimation in coastal domestic emission control areas using high spatial-temporal resolution data: A China case

It is necessary to accurately calculate ship carbon emissions for shipping suitability. The state-of-the-art approaches could arguably not be able to estimate ship carbon emissions accurately due to the uncertainties of Ship Technical Specification Database (STSD) and the geographical and temporal breakpoints in Automatic Identification System (AIS) data, hence requiring a new methodology to be developed to address such defects and further improve the accuracy of emission estimation. Firstly, a novel STSD iterative repair model is proposed based on the random forest algorithm by the incorporation of13 ship technical parameters. The repair model is scalable and can substantially improve the quality of STSD. Secondly, a new ship AIS trajectory segmentation algorithm based on ST-DBSCAN is developed, which effectively eliminates the impact of geographical and temporal AIS breakpoints on emission estimation. It can accurately identify the ships' berthing and anchoring trajectories and reasonably segment the trajectories. Finally, based on this proposed framework, the ship carbon dioxide emissions within the scope of domestic emission control areas (DECA) along the coast of China are estimated. The experiment results indicate that the proposed STSD repair model is highly credible due to the significant connections between ship technical parameters. In addition, the emission analysis shows that, within the scope of China's DECA, the berthing period of ships is longer owing to the joint effects of coastal operation features and the strict quarantine measures under the COVID-19 pandemic, which highlights the emissions produced by ship auxiliary engines and boilers. The carbon intensity of most coastal provinces in China is relatively high, reflecting the urgent demand for the transformation and updates of the economic development models. Based on the theoretical models and results, this study recommends a five-stage decarbonization scheme for China's DECA to advance its decarbonization process.

Assessment Method Based on AIS Data Combining the Velocity Obstacle Method and Pareto Selection for the Collision Risk of Inland Ships

A ship collision risk assessment model is an essential part of ship safety navigation. At present, the open water collision risk assessment model (such as the closest point of approach) is applied, but a ship collision risk model suitable for inland rivers is still in the exploration stage. Compared with open waters, the inland waterway has a larger density of ships, and the land and water environments are complex. The existing risk assessment models lack adaptability under the conditions of inland navigation. Therefore, this paper proposes a real-time collision risk assessment method for ships navigating inland rivers. This method utilizes the information of ships’ size in the automatic identification system (AIS) to construct the velocity obstacle cone between convex polygonal targets using the velocity obstacle method. Then, according to the geometric relationship between the relative velocity of two targets and the velocity obstacle cone, a new collision risk assessment model is defined. This model defines two indicators to evaluate the navigation collision risk: the degree of velocity obstacle intrusion (DVOI) and time of velocity obstacle intrusion (TVOI). These two indicators assess the risk of collision, respectively, from two aspects speed and course. In addition, a method using a trajectory compression algorithm to screen collision avoidance operation points in ship AIS trajectory is proposed to screen collision avoidance scenarios in the Yangtze River waterway. The effectiveness of the proposed collision risk model is verified in course-keeping and collision avoidance scenarios and compared with the traditional closest point of approach (CPA) method. The results indicate that the evaluation model for collision risk assessment is more accurate than the CPA method in all scenarios. Finally, this paper uses the Pareto selection algorithm to combine DVOI and TVOI, which can identify the ship that poses the greatest risk to our ship.

AIS Trajectories Simplification Algorithm Considering Topographic Information.

With the development of maritime technology and equipment, most ships are equipped with an automatic identification system (AIS) to store navigation information. Over time, the size of the data increases, rendering its storage and processing difficult. Hence, it is necessary to transform the AIS data into trajectories, and then simplify the AIS trajectories to remove unnecessary information that is not related to route shape. Moreover, topographic information must be considered because otherwise, the simplified trajectory can intersect obstacles. In this study, we propose an AIS trajectory simplification algorithm considering topographic information. The proposed algorithm simplifies the trajectories without the intersection of the trajectory and obstacle using the improved Douglas–Peucker algorithm. Polygon map random (PMR) quadtree was used to consider topographic information on the coast, and the intersection between topographic information and simplified trajectories was efficiently computed using the PMR quadtree. To verify the effectiveness of the proposed algorithm, experiments were conducted on real-world trajectories in the Korean sea. The proposed algorithm yielded simplified trajectories with no intersections of the trajectory and obstacle. In addition, the computational efficiency of the proposed algorithm with the PMR quadtree was superior to that without the PMR quadtree.

MASS autonomous navigation system based on AIS big data with dueling deep Q networks prioritized replay reinforcement learning

Ship autonomous navigation constitutes the most critical step in intelligent ships and is the major prerequisite for the all task completion of the marine autonomous surface ship (MASS). Reinforcement learning (RL) has been widely used in unmanned transport vehicles because of its excellent performance in solving continuous handling problems. This study proposes a MASS autonomous navigation system using dueling deep Q networks prioritized replay (Dueling-DQNPR) based on the ship automatic identification system (AIS) big data. A navigation environment with three difficulty levels were established to train the Dueling-DQNPR network in sequence by setting a reward mechanism. Moreover, the Dueling-DQNPR was improved by combining the prioritized experience replay, dueling structure and long–short-term memory unit to increase the network depth and ability to process continuous data. Finally, simulation training for the AIS trajectory data was carried out in waters near Zhoushan port. The results demonstrated that, through trial and error, the MASS could be controlled to arrive at its destination without collision. As a result, the proposed method may be applicable to MASS on-duty technology and intelligent ships.

Inland waterway network mapping of AIS data for freight transportation planning

AbstractTravel demand models (TDMs) with freight forecasts estimate performance metrics for competing infrastructure investments and potential policy changes. Unfortunately, freight TDMs fail to represent non-truck modes with levels of detail adequate for multi-modal infrastructure and policy evaluation. Recent expansions in the availability of maritime movement data, i.e. Automatic Identification System (AIS), make it possible to expand and improve representation of maritime modes within freight TDMs. AIS may be used to track vessel locations as timestamped latitude–longitude points. For estimation, calibration and validation of freight TDMs, this work identifies vessel trips by applying network mapping (map-matching) heuristics to AIS data. The automated methods are evaluated on a 747-mile inland waterway network, with AIS data representing 88% of vessel activity. Inspection of 3820 AIS trajectories was used to train the heuristic parameters including stop time, duration and location. Validation shows 84⋅0% accuracy in detecting stops at ports and 83⋅5% accuracy in identifying trips crossing locks. The resulting map-matched vessel trips may be applied to generate origin–destination matrices, calculate time impedances, etc. The proposed methods are transferable to waterways or maritime port systems, as AIS continues to grow.

Maritime Traffic Route Detection Framework Based on Statistical Density Analysis From AIS Data Using a Clustering Algorithm

Maritime traffic routes by ships navigation vary according to country and geographic characteristics, and they differ according to the characteristics of the ships. In ocean areas adjacent to coasts, regulated routes are present, e.g., traffic separation scheme for ships entering and leaving; however, most ocean areas do not have such routes. Maritime traffic route research has been conducted based on computer engineering to create routes; however, ship characteristics were not considered. Thus, this article proposes a framework to generate maritime traffic routes using statistical density analysis. Here, automatic identification system (AIS) data are used to derive quantitative traffic routes. Preprocessing is applied to the AIS data, and a similar ship trajectory pattern is decomposed into a matrix based on the Hausdorff–distance algorithm and then stored in a database. A similar pattern makes the AIS trajectory simple using the Douglas–Peucker algorithm. In addition, density-based spatial clustering of applications with noise (DBSCAN) is performed to identify the waypoints of vessels then create routes by connecting waypoints. The width of maritime routes created based on a similar ship trajectory is subjected to kernel density estimation analysis (KDE). Then, waypoints evaluation of the main route is performed from the results of KDE 75% and 90% considering the statistical in the total maritime traffic, and the results applied to the targeted ocean area are compared. Finally, the result of KDE 90% of maritime traffic with framework analyzed the safety route, which can be a basis for developing routes for maritime autonomous surface ships.

Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

Automatic Identification System (AIS) data have been widely used in many fields, such as collision detection, navigation, and maritime traffic management. Similarity analysis is an important process for most AIS trajectory analysis topics. However, most traditional AIS trajectory similarity analysis methods calculate the distance between trajectory points, which requires complex and time-consuming calculations, often leading to substantial errors when processing AIS trajectory data characterized by substantial differences in length or uneven trajectory points. Therefore, we propose a cell-based similarity analysis method that combines the weight of the direction and k-neighborhood (WDN-SIM). This method quantifies the similarity between trajectories based on the degree of proximity and differences in motion direction. In terms of its effectiveness and efficiency, WDN-SIM outperformed seven traditional methods for trajectory similarity analysis. Particularly, WDN-SIM has a high robustness to noise and can distinguish the similarities between trajectories under complex situations, such as when there are opposing directions of motion, large differences in length, and uneven point distributions.

Ship Short-Term Trajectory Prediction Based on RNN

In the application of ship supervision, ship collision avoidance, maritime search and rescue, the trajectory prediction of the target ship is a key issue. Given the ship navigation trajectory is easily affected by wind and waves, in order to improve the accuracy and efficiency of prediction, a ship short-term trajectory prediction method combining with Automatic Identification System (AIS) data and deep learning is proposed. Based on the preprocessing of AIS data, a recurrent neural network (RNN) model is constructed to achieve the accurate prediction of ship position information is realized. Through the real ship AIS trajectory data experiment, the results show that the method is practical and effective. Compared with the traditional backpropagation (BP) neural network processing method, it has certain advantages in prediction accuracy.

Data mining approach for automatic ship-route design for coastal seas using AIS trajectory clustering analysis

In this paper, we propose an automatic route design method based on simple recurrent unit (SRU) and automatic identification system (AIS) data. Laplacian eigen maps and Gaussian kernel functions are used to compress the AIS data and extract the turning points of all ships. Fuzzy adaptive density-based spatial clustering of applications with noise (FA-DBSCAN) technique is used to cluster the turning points obtained at the preprocessing stage to obtain the turning region. Optimal turn region matching is used to connect the turning regions of similar routes, and the SRU neural network algorithm is used to learn the relationship between different types, sizes, and drafts of ships in each turning region; extract the feature-turning points; and obtain the recommended coastal routes, speed, and course of each type of ship. In the experimental stage, a large variety of AIS data from two sea areas are used to compare and analyze the designed route and real-ship data through LSTM and SRU experiments. The results show that the SRU algorithm improves the training speed and accuracy in comparison to LSTM, while the generated automatic route meets the requirements of navigation practice.

An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

With the enormous amount of information provided by the ship Automatic Identification System (AIS), AIS is now playing a significant role in maritime transport system-related research and development. Many kinds of research and industrial applications are based on the ship trajectory extracted from raw AIS data. However, due to the issues of equipment, the transmission environment, and human factors, the raw AIS data inevitably contain abnormal messages, which have hindered the utilization of such information in practice. Thus, in this paper, an anomaly detection method that focuses on AIS trajectory is proposed, making comprehensive use of the kinematic information of the ship in the AIS data. The method employs three steps to obtain non-error AIS trajectories: (1) data preprocessing, (2) kinematic estimation, and (3) error clustering. It should be noted that steps (2) and (3) are involved in an iterative process to determine all of the abnormal data. A case study is then conducted to test the proposed method on real-world AIS data, followed by a comparison between the proposed method and the rule-based anomaly detection method. As the processed trajectories show fewer abnormal features, the results indicate that the method improves performance and can accurately detect as much abnormal data as possible.

Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

The Automatic Identification System (AIS) of ships provides massive data for maritime transportation management and related researches. Trajectory clustering has been widely used in recent years as a fundamental method of maritime traffic analysis to provide insightful knowledge for traffic management and operation optimization, etc. This paper proposes a ship AIS trajectory clustering method based on Hausdorff distance and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), which can adaptively cluster ship trajectories with their shape characteristics and has good clustering scalability. On this basis, a re-clustering method is proposed and comprehensive clustering performance metrics are introduced to optimize the clustering results. The AIS data of the estuary waters of the Yangtze River in China has been utilized to conduct a case study and compare the results with three popular clustering methods. Experimental results prove that this method has good clustering results on ship trajectories in complex waters.

Trajectory Data Restoring: A Way of Visual Analysis of Vessel Identity Base on OPTICS

Automatic identification system (AIS) data is a significant analysis and decision-making basis for maritime situational awareness. Because of particular navigation environment and the vulnerability of AIS equipment onboard, results in the phenomenon that numerous vessels share the same Maritime Mobile Service Identity (MMSI) in the AIS data collected in ocean and inland waterway. This kind of mixed trajectory information dramatically affects the judgement of the maritime manager and supervisors. In this paper, the visual analytics combined with the algorithm named Ordering Points to Identify the Clustering Structure (OPTICS) is adopted to realize the separation of vessels sharing same MMSI, which can help analysts to recognize the vessel trajectory information and assess the risk of marine traffic correctly. Firstly, this paper illustrates the application of OPTICS clustering method based on space-time distance in AIS trajectory separation. Secondly, the display and interaction of trajectory information of Vessels sharing the same MMSI in OpenStreetMap map were introduced. Then visual analysis method is applied to optimize the parameters of the algorithm and display the trajectory separation effect corresponding to different settings. In final, various practical situations are discussed, and the empirical test shows that it is feasible in AIS chaos trajectory separation.

Navigational risk analysis based on GIS spatiotemporal trajectory mining: a case study in Nanjing Yangtze River Bridge waters

With the continuous growth of the quantity, scale, and speed of vessels in recent years, maritime accidents are posing increasing risks to societies and individuals, especially in narrow inland waterways. Therefore, it is of great significance to analyze navigational risks to ensure the safety of waterborne transportation. In this paper, the navigational risks of Nanjing Yangtze River Bridge (NYRB) waters are investigated based on spatiotemporal mining on massive automatic identification system (AIS) trajectories by using geographic information system (GIS) techniques. A time-series-oriented trajectory processing method is proposed to deal with the historical AIS data in the whole year of 2019. The method adopts a periodic processing strategy to produce traffic density estimation products in multiple temporal scales for supporting spatiotemporal analysis. The proposed method greatly improves the data-processing efficiency and provides a flexible way to deeply understand the vessel behavior patterns in NYRB waters. Then the complete characteristics of the spatial distribution and temporal variation of AIS trajectories are revealed. Based on that, three types of critical navigational risks are discovered, which include the safety distance risk, the pier collision risk, and the traffic congestion risk. Moreover, we find that the greatest risk is existed in small vessels in the flood season, which is worth the most concern.

Traffic Inequality and Relations in Maritime Silk Road: A Network Flow Analysis

Maritime traffic can reflect the diverse and complex relations between countries and regions, such as economic trade and geopolitics. Based on the AIS (Automatic Identification System) trajectory data of ships, this study constructs the Maritime Silk Road traffic network. In this study, we used a complex network theory along with social network analysis and network flow analysis to analyze the spatial distribution characteristics of maritime traffic flow of the Maritime Silk Road; further, we empirically demonstrate the traffic inequality in the route. On this basis, we explore the role of the country in the maritime traffic system and the resulting traffic relations. There are three main results of this study. (1) The inequality in the maritime traffic of the Maritime Silk Road has led to obvious regional differences. Europe, west Asia, northeast Asia, and southeast Asia are the dominant regions of the Maritime Silk Road. (2) Different countries play different maritime traffic roles. Italy, Singapore, and China are the core countries in the maritime traffic network of the Maritime Silk Road; Greece, Turkey, Cyprus, Lebanon, and Israel have built a structure of maritime traffic flow in the eastern Mediterranean Sea, and Saudi Arabia serves as a bridge for maritime trade between Asia and Europe. (3) The maritime traffic relations show the characteristics of regionalization; countries in west Asia and the European Mediterranean region are clearly polarized, and competition–synergy relations have become the main form of maritime traffic relations among the countries in the dominant regions. Our results can provide a scientific reference for the coordinated development of regional shipping, improvement of maritime competition, cooperation strategies for countries, and adjustments in the organizational structure of ports along the Maritime Silk Road.