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Developing contextually aware ship domains using machine learning

Developing contextually aware ship domains using machine learning
Developing contextually aware ship domains using machine learning
Developing risk models to predict where collisions between vessels might occur is hindered by the relative sparsity of collisions. To address this, vessel encounters and near-misses have been used as a surrogate indicator of collision risk, referred to as ‘domain analysis’. When constructed empirically, using historical information, previous work is challenged by the multitude of factors which influence the passing distances between vessels. Within this paper, we conduct data mining of big vessel traffic datasets to determine the encounter characteristics across different waterways, vessel types and speeds, weather conditions and other exploratory variables. To achieve this, we utilise a novel approach of machine learning through a random forest algorithm to predict the critical passing distance between vessels in a multitude of conditions. We contribute a far greater range of influencing factors on domain size and shape than previous studies. Finally, we investigate the potential advantages of this approach to assess the spatial risk of collision across a large region. The results help to establish the factors that influence collision risk between navigating vessels and enable empirical maritime risk assessments.
automatic identification system, ship behaviour, ship collision, ship domain
0373-4633
515-532
Rawson, Andrew David
2f5d38d7-f4c9-45f5-a8de-c7f91b8f68c7
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Rawson, Andrew David
2f5d38d7-f4c9-45f5-a8de-c7f91b8f68c7
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c

Rawson, Andrew David and Brito, Mario (2021) Developing contextually aware ship domains using machine learning. Journal of Navigation, 74 (3), 515-532. (doi:10.1017/S0373463321000047).

Record type: Article

Abstract

Developing risk models to predict where collisions between vessels might occur is hindered by the relative sparsity of collisions. To address this, vessel encounters and near-misses have been used as a surrogate indicator of collision risk, referred to as ‘domain analysis’. When constructed empirically, using historical information, previous work is challenged by the multitude of factors which influence the passing distances between vessels. Within this paper, we conduct data mining of big vessel traffic datasets to determine the encounter characteristics across different waterways, vessel types and speeds, weather conditions and other exploratory variables. To achieve this, we utilise a novel approach of machine learning through a random forest algorithm to predict the critical passing distance between vessels in a multitude of conditions. We contribute a far greater range of influencing factors on domain size and shape than previous studies. Finally, we investigate the potential advantages of this approach to assess the spatial risk of collision across a large region. The results help to establish the factors that influence collision risk between navigating vessels and enable empirical maritime risk assessments.

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AR_EmpiricalDomains_03 - Accepted Manuscript
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e-pub ahead of print date: 8 March 2021
Keywords: automatic identification system, ship behaviour, ship collision, ship domain

Identifiers

Local EPrints ID: 447728
URI: http://eprints.soton.ac.uk/id/eprint/447728
ISSN: 0373-4633
PURE UUID: e8348c7a-eac6-470b-86e5-6abf6c6304f0
ORCID for Andrew David Rawson: ORCID iD orcid.org/0000-0002-8774-2415
ORCID for Mario Brito: ORCID iD orcid.org/0000-0002-1779-4535

Catalogue record

Date deposited: 19 Mar 2021 17:30
Last modified: 28 Apr 2022 04:18

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Contributors

Author: Andrew David Rawson ORCID iD
Author: Mario Brito ORCID iD

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