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

Zhang, Wei-Bin; Goerlandt, Floris; Montewka, Jakub; Kujala, Pentti

doi:10.1016/j.oceaneng.2015.07.046

Cited by 196 publications

(97 citation statements)

References 43 publications

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“…However, the risk of vessel collisions depends on many other factors, including humans' judgement. To further verify our results, this paper also employed the vessel conflict ranking operator (VCRO) model developed by Zhang et al (2015). …”

Section: Discussionmentioning

confidence: 91%

“…For example, Chin and Debnath (2009) proposed a probit model using two critical parameters: distance at closest point of approach and time to closest point of approach; and Zhang et al (2015) proposed to use vessel conflict ranking operator (VCRO), which is estimated by a survey among marinetime experts, considering only three parameters of the correlation of two vessels: relative distance, relative speed and relative phase. However, the aforementioned methods all require a comprehensive survey on vessel operations among vessel pilots and maritime experts.…”

Section: Introductionmentioning

confidence: 99%

“…Mou, et al, 2010;Weng et al, 2014aWeng et al, , 2014bZhang et al, 2015) and vessel traffic (e.g., Xiao et al, 2015). AIS helps vessels "see" each other in the channel by transmitting information between vessels, from vessels to shore or vice versa.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of waterway transportation in Southeast Texas waterway based on AIS data

Mehta

Zaloom

et al. 2016

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Section: Discussionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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Analysis of waterway transportation in Southeast Texas waterway based on AIS data

Mehta

Zaloom

et al. 2016

Ocean Engineering

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“…The sizes and shapes of theses domains range from very simple to quite complex structures, some are circular, elliptical or polygonal, some are static, and others are dynamically resized depending on the speed of the vessel, e.g. Fujii & Tanaka (1971), Goodwin (1975), Coldwell (1983, Zhao et al (1993), Pedersen (1995), Mestl et al (2008), Pietrzykowski (2008), Pietrzykowski & Uriasz (2009), Wang et al (2009), Zhang et al (2015 in order to make the term "near collision" and "comfort zone" more tangible and quantifiable.…”

Section: Dnv-gl Research and Innovation -It Analytics Høvik Norwaymentioning

confidence: 99%

Identifying and Analyzing Safety Critical Maneuvers from High Resolution AIS Data

Mestl¹,

Tallakstad²,

Castberg³

2016

TransNav

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“…The annual amount of cargo is about 1 billion tonnes, and the ferries carry approximately 100 million passengers. The main research applications have been general spatiotemporal characterization and statistical analyses of the traffic systems in some sea areas [19] and the quantification of ship encounters and associated collision risks (e.g., [20]).…”

Section: Introductionmentioning

confidence: 99%

Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System

Similä

Lensu

2018

Remote Sensing

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The automatic identification system (AIS) was developed to support the safety of marine traffic. In ice-covered seas, the ship speeds extracted from AIS data vary with ice conditions that are simultaneously reflected by features in synthetic aperture radar (SAR) images. In this study, the speed variation was related to the SAR features and the results were applied to generate a chart of expected speeds from the SAR image. The study was done in the Gulf of Bothnia in March 2013 for ships with ice class IA Super that are able to navigate without icebreaker assistance. The speeds were normalized to dimensionless units ranging from 0 to 10 for each ship. As the matching between AIS and SAR was complicated by ice drift during the time gap (from hours to two days), we calculated a set of local statistical SAR features over several scales. Random forest tree regression was used to estimate the speed. The accuracy was quantified by mean squared error and by the fraction of estimates close to the actual speeds. These depended strongly on the route and the day. The error varied from 0.4 to 2.7 units 2 for daily routes. Sixty-five percent of the estimates deviated by less than one speed unit and 82% by less than 1.5 speed units from the AIS speeds. The estimated daily mean speeds were close to the observations. The largest speed decreases were provided by the estimator in a dampened form or not at all. This improved when the ice chart thickness was included as a predictor.

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

Cited by 196 publications

References 43 publications

Analysis of waterway transportation in Southeast Texas waterway based on AIS data

Analysis of waterway transportation in Southeast Texas waterway based on AIS data

Identifying and Analyzing Safety Critical Maneuvers from High Resolution AIS Data

Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System

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