Exploring AIS data for intelligent maritime routes extraction

Zhuang, Y; Xiao, Yijia; Cheng, Liang; He, Rong; Ruan, Xiaojun; Zhou, Xiao; Li, Manchun; Ran, Bin

doi:10.1016/j.apor.2020.102271

Cited by 97 publications

(27 citation statements)

References 43 publications

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“…A similar topic was also studied in Wu et al [20]. More studies using the AIS data were well introduced by Svanberg et al [21], and the most recent studies are found in Liu et al [22], Yan et al [23], Wu et al [24], and Yan et al [25].…”

Section: Related Workmentioning

confidence: 82%

Operational Analysis of Container Ships by Using Maritime Big Data

Roh

Park

et al. 2021

JMSE

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The shipping company or the operator determines the mode of operation of a ship. In the case of container ships, there may be various operating patterns employed to arrive at the destination within the stipulated time. In addition, depending on the influence of the ocean’s environmental conditions, the speed and the route can be changed. As the ship’s fuel oil consumption is closely related to its operational pattern, it is possible to identify the most economical operations by analyzing the operational patterns of the ships. The operational records of each shipping company are not usually disclosed, so it is necessary to estimate the operational characteristics from publicly available data such as the automatic identification system (AIS) data and ocean environment data. In this study, we developed a visualization program to analyze the AIS data and ocean environmental conditions together and propose two categories of applications for the operational analysis of container ships using maritime big data. The first category applications are the past operation analysis by tracking previous trajectories, and the second category applications are the speed pattern analysis by shipping companies and shipyards under harsh environmental conditions. Thus, the operational characteristics of container ships were evaluated using maritime big data.

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Section: Related Workmentioning

confidence: 82%

Operational Analysis of Container Ships by Using Maritime Big Data

Roh

Park

et al. 2021

JMSE

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“…Lubcke et al [6] and Shabani et al [7] focus on optimisation of a SAR operation. Modern challenges and technological advancements in communications systems applied in maritime rescue are analysed by Vlacic et al [8], Lazaro et al [9], Gozlan et al [10], and Yan et al [11]. Wang et al [12] describe an innovative approach to missing data filling through multiple imputation.…”

Section: Literature Overview (State Of the Art)mentioning

confidence: 99%

“…Remote Sens. 2021, 13, 3151 3 of 17 communications systems applied in maritime rescue are analysed by Vlacic et al [8], Lazaro et al [9], Gozlan et al [10], and Yan et al [11]. Wang et al [12] describe an innovative approach to missing data filling through multiple imputation.…”

Section: Ais Datamentioning

confidence: 99%

Multi-Criteria Selection of Surface Units for SAR Operations at Sea Supported by AIS Data

Wielgosz

Małyszko

2021

Remote Sensing

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The authors discuss currently conducted research aimed at improving the planning and performance of search and rescue (SAR) operations at sea. The focus is on the selection of surface units in areas of high traffic density. A large number of ships in the area of distress can make the process of selection of best suited vessels longer. An analysis of features which may render a vessel unsuitable for the job, depending on the area and type of operation, has been conducted. Criteria of assessment and selection of ships have been described, preceded by an expert analysis. The selection process has been made using Multi-Criteria Decision Analysis (MCDA). The authors propose to apply officially available data from the Automatic Identification System (AIS)—a sensor for the ECDIS and other electronic chart systems—in the analysis of the availability of ships. Algorithms filtering available units have been built and applied in a simulation, using real AIS data, of one of the most common types of SAR operations. The method is proposed as an enhancement of decision support systems in maritime rescue services.

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“…(Arguedas et al 2018) proposed a maritime traffic knowledge discovery and representation system, and the generated maritime traffic network can be used to detect abnormal behaviors of vessels. (Yan et al 2020) proposed a maritime route extraction method based on AIS data, and the maritime traffic routes extracted can be used for abnormal ship behavior detection. (Wang et al 2020) proposed a parameter-light rare behavior detection method and presented experiments on simulated data sets and real AIS data sets.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Ship Behavior Detection after the Closure of AIS Based on Radar Data

Zhang

Liu

Wen³

2021

Preprint

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In marine transportation, most ships are equipped with AIS devices. The AIS data sent by these devices can help maritime authorities to manage ships in relevant sea areas. However, AIS is a self-reporting system, when a ship is engaged in illegal activities, the AIS device may be turned off. Therefore, after the AIS is closed, if the ship's behavior during a certain period of time is different from the ship's behavior before the closure of AIS, the different behavior is likely to represent that the ship is conducting illegal activities. This behavior is considered abnormal and needs to be detected in time. Based on radar trajectory data, the detection of abnormal ship behavior is studied from two aspects: speed and direction. In order to improve the intelligent level of abnormal ship behavior detection, the abnormal speed behavior detection algorithm combined with rules and clustering (ASBD-RC) and the abnormal direction behavior detection algorithm combined with partition and the earth mover's distance (ADBD-PE) are proposed. The ASBD-RC algorithm can reduce the influence of noise and sea clutter on abnormal speed behavior detection. The ADBD-PE algorithm can effectively partition and identify trajectory segments with abnormal direction. In the experiment, based on the real and simulated radar trajectories, the abnormal behaviors of ships under different scenarios are generated. The experimental results show that in most scenarios, the ASBD-RC algorithm and the ADBD-PE algorithm can effectively detect abnormal ship behavior. And compared with other algorithms, the proposed two algorithms have better and more stable detection results.

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Exploring AIS data for intelligent maritime routes extraction

Cited by 97 publications

References 43 publications

Operational Analysis of Container Ships by Using Maritime Big Data

Operational Analysis of Container Ships by Using Maritime Big Data

Multi-Criteria Selection of Surface Units for SAR Operations at Sea Supported by AIS Data

Abnormal Ship Behavior Detection after the Closure of AIS Based on Radar Data

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