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A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Merchant Marine College, Shanghai Maritime University, Shanghai, China. (Skövde Artificial Intelligence Lab (SAIL))
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2900-9335
Merchant Marine College, Shanghai Maritime University, Shanghai, China.
2017 (English)In: Ocean Engineering, ISSN 0029-8018, E-ISSN 1873-5258, Vol. 145, p. 492-501Article in journal (Refereed) Published
Abstract [en]

Multi-vessel collision risk assessment for maritime traffic surveillance is a key technique to ensure the safety and security of maritime traffic and transportation. This paper proposes a framework of real-time multi-vessel collision assessment that combines a spatial clustering process (DBSCAN) for detecting clusters of encounter vessels and a multi-vessel collision risk index model for encounter vessels within each cluster from the large amounts of monitored vessels in a surveyed sea area. First, the vessels monitored are clustered using DBSCAN to obtain the clusters of encounter vessels, filtering out the relatively safe vessels. Then, the dynamic motion relation between encounter vessels within each cluster is modeled to obtain DCPA and TCPA. The semantic and mathematical relationship of vessel collision risk index for each cluster of encounter vessels with DCPA and TCAP is constructed using a negative exponential function. To illustrate the effectiveness of the framework proposed, an experimental case study has been carried out within the west coastal waters of Sweden. The results show that our framework is effective and efficient at detecting and ranking collision risk indexes between encounter vessels within each duster, which allows an automatic risk prioritization of encounter vessels for further investigation by operators. Hence, this framework can improve the safety and security of vessel traffic transportation and reduce the loss of lives and property.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 145, p. 492-501
Keyword [en]
Maritime transportation, Vessel traffic, AIS, Collision risk index, Maritime surveillance
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
URN: urn:nbn:se:his:diva-14544DOI: 10.1016/j.oceaneng.2017.09.015ISI: 000414886600041Scopus ID: 2-s2.0-85029783882OAI: oai:DiVA.org:his-14544DiVA, id: diva2:1162240
Projects
KK Prospekt NOVA 2014/0294China Scholarship 366 Council, Grant number 201608310093PhD candidate 367 in Shanghai Maritime University, Grant number 2016ycx077
Funder
Knowledge Foundation, 20140294
Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2018-06-11Bibliographically approved

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Zhen, RongRiveiro, Maria

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