Analysis Of Automatic Identification System Data Research Articles

Research on AIS Data Analysis and Its Integration into Discipline-specific Teaching

The processing of ship AIS (Automatic Identification System) data plays a crucial role in the digital transformation of the shipping industry. From data collection to data storage, data analysis, and data visualization, AIS data processing technology provides robust support for the shipping sector. In terms of data collection, the platform utilizes distributed message queues to aggregate AIS data. For data storage, the platform efficiently manages and stores massive amounts of AIS data using data warehouses. When it comes to data analysis, the platform mines and analyzes AIS data through Lambda, extracting valuable information such as ship trajectories and sailing speeds, thereby supporting areas such as ship management, navigation safety, and marine research. In data visualization, the platform intuitively displays ship navigation status through charts and maps, providing strong support for decision-making. As an essential component of big data applications, AIS data analysis technology is playing an increasingly important role in the shipping industry. The big data technology major of Jiangsu Maritime Institute integrates big data applications like AIS data analysis into its curriculum, taking the upgrading needs of the shipping industry as an entry point. It actively plans the cultivation of composite big data skilled talents under the backdrop of smart shipping, comprehensively serving the upgrading of the shipping industry and promoting the transformation from a major shipping country to a shipping powerhouse.

Overview of machine learning methods in maritime traffic monitoring based on Automatic Identification System

The Automatic Identification System (AIS) was introduced to increase maritime safety. Its messages contain information on the identification, position, speed and course of ships. AIS enables the tracking of ships, the establishment of corridors, the detection of unusual manoeuvres (piracy, terrorism, problems with the ship or crew), the optimisation of shipping channels, strategic planning and the increase of shipping efficiency, as well as the reduction of pollution. AIS data is processed using various machine learning techniques for the purpose of extracting vessel trajectories, assessing collision risk, detecting maritime anomalies and analysing ship emissions to the environment. The development of machine learning techniques has a large and significant impact because they can quickly process a large amount of data and thus increase the safety and reliability of maritime transport. The paper gives an overview of the technical features and benefits of using AIS as well as the analysis of AIS data, algorithms for classifying and clustering AIS data and machine learning methods for detecting anomalies in maritime traffic.

Spatial patterns of fishing activity inside the Gorringe bank MPA based on VMS, AIS and e-logbooks data

In this study, the fishing distribution and intensity over the Gorringe bank, a large oceanic underwater mountain located in the NE Atlantic, Natura 2000 Network MPA inside Portugal‘s EEZ is evaluated in the period from 2012 to 2016. Based on the combined analysis of fisheries-dependent data comprising Vessel Monitoring System (VMS), Automatic Identification System (AIS) and electronic logbooks available for the Portuguese fleet operating within the area, it was possible to identify fishing trips and fishing events and define fishing trajectories, allowing the estimation of the fishing effort and the CPUE at a high spatial resolution. A total of 32 Portuguese vessels were identified to have undertaken fishing activity within the limits of this MPA, mainly longliners based on mainland ports, operating drifting and bottom longlines on a seasonal basis. Fleet-related differences in the distribution and intensity of fishing effort and CPUE for the main target species were addressed. The bottom longline activity concentrated, as expected, around the two seamount summits, where the presence of VMEs and sensitive habitat-forming species have been registered. The analysis of AIS data, the only dataset made available in this study for non-Portuguese vessels, strongly suggests that the foreign fleet operating within the Gorringe MPA represents a significant fraction of the fishing activity in the area. If assessing the fishing impacts is deemed as an important objective in the management of this MPA, then the results obtained in this study can be a valuable contribution towards the environmental protection of this oceanic seamount.

Applications of AIS data analysis in bunkering Industry and its related factors

In the ocean fuel transportation industry, the optimization of bunkering operations depends on price, position, delays from the expected route, and the cost of delays caused by the bunker operations themselves. Despite their potential importance, detailed bunker activity statistics at each port of call (e.g: waiting time, barge capacity, berth, or berth) are not available. A greater hid deviation from the usual method is that the provider, therefore, arrives past due for a bunker mission. The purchaser commonly has a time slot wherein it anchors as much as acquire bunker. If the bunker barge arrives past due, the purchaser can be beneath neath time strain because of closing dates for shipment, which may also reason the purchaser to interrupt the bunker operation earlier than the agreed quantity of bunker is supplied. This is obtainable confined interest from transport businesses and charterers because the fraudulent quantity is thought to be negligible. However, studies have recommended that the aggregated quantity of misplaced bunker is of big value. To screen such conduct we intend to make use of Automatic Identification System (AIS) statistics from bunker barges and numerous vessels they supply.

Impact of Distance Measures on the Performance of AIS Data Clustering

Automatic Identification System (AIS) data stream analysis is based on the AIS data of different vessel’s behaviours, including the vessels’ routes. When the AIS data consists of outliers, noises, or are incomplete, then the analysis of the vessel’s behaviours is not possible or is limited. When the data consists of outliers, it is not possible to automatically assign the AIS data to a particular vessel. In this paper, a clustering method is proposed to support the AIS data analysis, to qualify noises and outliers with respect to their suitability, and finally to aid the reconstruction of the vessel’s trajectory. In this paper, clustering results have been obtained using selected algorithms, including k-means, k-medoids, and fuzzy c-means. Based on the clustering results, it is possible to decide on the qualification of data with outliers and on their usefulness in the reconstruction of the vessel trajectory. The main aim of this paper is to answer how different distance measures during a clustering process can influence AIS data clustering quality. The main core question is whether or not they have an impact on the process of reconstruction of the vessel trajectories when the data are damaged. The research question during the computational experiments asked whether or not distance measure influence AIS data clustering quality. The computational experiments have been carried out using original AIS data. In general, the experiment and the results confirm the usefulness of the cluster-based analysis when the data include outliers that are derived from the natural environment. It is also possible to monitor and to analyse AIS data using clustering when the data include outliers. The computational experiment results confirm that the k-means with Euclidean distance has the best performance.

Boat anchoring pressure on coastal seabed: Quantification and bias estimation using AIS data

Global shipping is economically important, but has many adverse environmental effects. Anchoring contributes greatly to this adverse impact, as it is responsible for mechanical disturbance of highly sensitive marine habitats. Recovery of these ecosystems is limited by slow regrowth. Anchoring pressure on coastal seabed habitats was estimated using AIS (Automatic Identification System) data along 1800km of Mediterranean coastline between 2010 and 2015. A comparison with field observations showed that these results were most consistent for large boats (>50m). An analysis of AIS data coupled with a seabed map showed that around 30% of the habitats between 0 and −80m exhibited anchoring pressure. Posidonia oceanica seagrass beds were the most impacted habitat in terms of duration. This methodology efficiently estimates spatial and temporal anchoring pressure principally due to large boats and should interest managers of marine protected areas as much as coastline managers.

AIS Data Visualization for Maritime Spatial Planning (MSP)

The constant increase in marine traffic and the simultaneous growth of the demand for exploiting marine areas (e.g., installing offshore wind power plants) require an adequate planning strategy for managing high traffic volumes. Maritime Spatial Planning (MSP) is the process of public development of an allocation plan for distributing, both spatially and temporally, human activities in marine areas. The adoption of e-Navigation is a possible solution for improving safety and security at sea by integrating maritime information on board and ashore. Automatic Identification System (AIS) data represents a fundamental source of information, since the analysis of AIS data can highlight the presence of congested areas as well as of illegal actions, such as smuggling, pollution, and unauthorized phishing in protected areas. Indeed, those activities are often characterized by abnormal manoeuvres that can be recognized by analyzing the routes of the vessels. However, the huge dimension of the AIS data to process requires the adoption of careful strategies for the data visualization. In this paper, we present a complete pipeline for visualizing ship routes from raw AIS data, which is a fundamental pre-requisite for carrying out a significant AIS-based route analysis, and describe a real case study, where 90 million AIS records, corresponding to one month of world-wide observations, are visualized using only open-source software.

Comparison study on AIS data of ship traffic behavior

AIS (Automatic Identification System) data provides valuable input parameters in ship traffic simulation models for maritime risk analysis and the prevention of shipping accidents. This article reports on the detailed comparisons of AIS data analysis between a Dutch case and a Chinese case. This analysis focuses on restricted waterways to support inland waterway simulations, comparing the differences between a narrow waterway in the Netherlands (the Port of Rotterdam) and a wide one in China (wide waterway of Yangtze River close to the Su-Tong Bridge). It is shown that straightforward statistical distributions can be used to characterize lateral position, speed, heading and interval times for different types and sizes of ships. However, the distributions for different characteristics of ship behaviors differ significantly.

Artificial Force Fields for Multi-agent Simulations of Maritime Traffic: A Case Study of Chinese Waterway

Collisions are an important factor in probabilistic risk modeling of waterway infrastructures. Especially in the design of bridges and terminals, the probability of ship collision is important. Researchers are looking for simulations that mimic ship behavior and estimate accident probabilities rather than using probabilistic accident models. AIS (Automatic Identification System) data forms a valuable basis for development of such simulation models. The data is a valuable asset for the validation and calibration of accident simulation models. Hence, a detailed analysis of AIS data is being undertaken as part of current research on ship collision simulations. Based on AIS data analysis, we developed a primary application of multi-agent simulation for ship traffic in the Yangtze River, which provides a realistic representation of the shipping traffic. Visual comparisons between AIS data and simulation data show comparable ship movements. More importantly, many different simulations show that the output of the simulation reproduces the behavior of real ships according to AIS data. Further down the road, ship failures will also be simulated, which results in a probability of ship accidents. Rather than the existing mathematical methods to estimate the risks of maritime accidents, multi-agent simulations provide a realistic ship behavior and all the details of ship movement. The model provides detailed information on how ships go wrong, and forms a useful tool in the design of waterways, design of bridges, and in traffic management.