Empirical Ship Domain based on AIS Data

Hansen, Martin G.; Jensen, Thomas Christian; Lehn-Schiøler, Tue; Melchild, Kristina; Rasmussen, Finn Mølsted; Ennemark, Finn

doi:10.1017/s0373463313000489

Cited by 164 publications

(114 citation statements)

References 10 publications

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“…While AIS data quality has been mediocre in its early implementation years (Graveson, 2004), the quality has improved significantly in recent years (Felski and Jaskolski, 2013;Felski et al, 2015), and further improvements are possible with proper antenna installation (Last et al, 2015). AIS data has been used for diverse purposes such as studying collision avoidance manoeuvers (Mou et al, 2010), delineating empirical ship domains (Hansen et al, 2013;Krzysztof and Gucma, 2012), studying navigational traffic patterns (Xiao et al, 2015), accident investigation , many maritime risk analysis methods Özbaş, 2013;Goerlandt and Montewka, 2015b;Mazaheri et al, 2014;Gucma et al, 2012) and models predicting ship performance in ice .…”

Section: Uses Of Ais Data In Maritime Transportation Researchmentioning

confidence: 99%

A method for detecting possible near miss ship collisions from AIS data

et al. 2015

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Section: Uses Of Ais Data In Maritime Transportation Researchmentioning

confidence: 99%

A method for detecting possible near miss ship collisions from AIS data

et al. 2015

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“…The shape of ship domain is set to a certain shape and the size of ship domain is considered to be almost constant in a voyage. In further research, People attempt at using a region of the shipʹs historical data (like the AIS data (Hansen, Jensen et al 2013))in a specific sea area to extract the specific information of ship domain. Since ship domain is a kind of image description of ship collision risk, it is reasonable to be changed with different ship speed and other navigation data.…”

Section: Ship Domain and Artificial Potential Fieldmentioning

confidence: 99%

Ship Domain Model for Multi-ship Collision Avoidance Decision-making with COLREGs Based on Artificial Potential Field

et al. 2017

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A multi-ship collision avoidance decision-making and path planning formulation is studied in a distributed way. This paper proposes a complete set of solutions for multi-ship collision avoidance in intelligent navigation, by using a top-to-bottom organization to structure the system. The system is designed with two layers: the collision avoidance decision-making and the path planning. Under the general requirements of the International Regulations for Preventing Collisions at Sea (COLREGs), the performance of distributed path planning decision-making for anti-collision is analyzed for both give-way and stand-on ships situations, including the emergency actions taken by the stand-on ship in case of the give-way ship's fault of collision avoidance measures. The Artificial Potential Field method(APF) is used for the path planning in details. The developed APF method combined with the model of ship domain takes the target ships' speed and course in-to account, so that it can judge the moving characteristics of obstacles more accurately. Simulation results indicate that the system proposed can work effectiveness.

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“…Ship domain 30 is an essential issue for the collision avoidance study. AIS data was used to compute the distances and 31 angles between the own ship and other ships, a minimum length and breadth of ship domain were analysed 32 to keep ship safe (Hansen, et al, 2012). 33…”

Section: Hu Et Al (2007) Developed a Live Ais Web System To Publishmentioning

confidence: 99%

A novel method for restoring the trajectory of the inland waterway ship by using AIS data

et al. 2015

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Abstract:The trajectory of the inland waterway ship is important and useful in analysing the features of 16 the ship behaviour and simulating traffic flows. In the proposed research, a method is designed to restore 17 the trajectory of an inland waterway ship based on the Automatic Identification System (AIS) data. Firstly, 18 three rules are developed to identify and remove the inaccurate data, based on the reception range of the 19 received AIS data and the manoeuvring characteristics of the inland waterway ship. Secondly, the method 20 of restoring the full trajectory incorporating navigational features of the inland waterway ship is proposed 21 to model the ship trajectory. The trajectory is characterised by three types (line, curve and arc) and five 22 steps (line, curve, arc, curve and line) during the turning section. In order to validate the proposed method, 23 the AIS data of two inland waterway ships collected from three AIS-base-stations is selected for the 24 analysis, all inaccurate AIS data is identified and removed by the use of three cleansing rules. The results 25show that the three developed rules can effectively identify the inaccurate AIS data. The AIS data collected 26 by an AIS-shipboard-unit is then used to: (1) restore the ship trajectory, and (2) to restore the full trajectory in an effective manner by using AIS data. 33

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Empirical Ship Domain based on AIS Data

Cited by 164 publications

References 10 publications

A method for detecting possible near miss ship collisions from AIS data

A method for detecting possible near miss ship collisions from AIS data

Ship Domain Model for Multi-ship Collision Avoidance Decision-making with COLREGs Based on Artificial Potential Field

A novel method for restoring the trajectory of the inland waterway ship by using AIS data

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