A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis

Gespeichert in:

Autor*in:	Rawson, Andrew [verfasserIn] Brito, Mario [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	accidents AIS data Machine learning maritime navigation safety risk assessment

Übergeordnetes Werk:	Enthalten in: Transport reviews - London [u.a.] : Taylor & Francis, 1981, 43(2023), 1, Seite 108-130
Übergeordnetes Werk:	volume:43 ; year:2023 ; number:1 ; pages:108-130

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1080/01441647.2022.2036864

Katalog-ID:	1839516283

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allfields	10.1080/01441647.2022.2036864 doi (DE-627)1839516283 (DE-599)KXP1839516283 DE-627 ger DE-627 rda eng Rawson, Andrew verfasserin aut A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis Andrew Rawson and Mario Brito 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier accidents (dpeaa)DE-206 AIS data (dpeaa)DE-206 Machine learning (dpeaa)DE-206 maritime (dpeaa)DE-206 navigation safety (dpeaa)DE-206 risk assessment (dpeaa)DE-206 Brito, Mario verfasserin aut Enthalten in Transport reviews London [u.a.] : Taylor & Francis, 1981 43(2023), 1, Seite 108-130 Online-Ressource (DE-627)301516936 (DE-600)1485107-6 (DE-576)273877801 1464-5327 nnns volume:43 year:2023 number:1 pages:108-130 https://www.tandfonline.com/doi/pdf/10.1080/01441647.2022.2036864 Verlag kostenfrei https://doi.org/10.1080/01441647.2022.2036864 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_206 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 43 2023 1 108-130 26 01 0206 4292208719 x1z 17-03-23 2403 01 DE-LFER 4321101018 00 --%%-- --%%-- n --%%-- l01 12-05-23 2403 01 DE-LFER https://doi.org/10.1080/01441647.2022.2036864 26 00 DE-206 Identifying and assessing the likelihood and consequences of maritime accidents has been a key focus of research within the maritime industry. However, conventional methods utilised for maritime risk assessment have been dominated by a few methodologies each of which have recognised weaknesses. Given the growing attention that supervised machine learning and big data applications for safety assessments have been receiving in other disciplines, a comprehensive review of the academic literature on this topic in the maritime domain has been conducted. The review encapsulates the prediction of accident occurrence, accident severity, ship detentions and ship collision risk. In particular, the purpose, methods, datasets and features of such studies are compared to better understand how such an approach can be applied in practice and its relative merits. Several key challenges within these themes are also identified, such as the availability and representativeness of the datasets and methodological challenges associated with transparency, model development and results evaluation. Whilst focused within the maritime domain, many of these findings are equally relevant to other transportation topics. This work, therefore, highlights both novel applications for applying these techniques to maritime safety and key challenges that warrant further research in order to strengthen this methodological approach.
spelling	10.1080/01441647.2022.2036864 doi (DE-627)1839516283 (DE-599)KXP1839516283 DE-627 ger DE-627 rda eng Rawson, Andrew verfasserin aut A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis Andrew Rawson and Mario Brito 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier accidents (dpeaa)DE-206 AIS data (dpeaa)DE-206 Machine learning (dpeaa)DE-206 maritime (dpeaa)DE-206 navigation safety (dpeaa)DE-206 risk assessment (dpeaa)DE-206 Brito, Mario verfasserin aut Enthalten in Transport reviews London [u.a.] : Taylor & Francis, 1981 43(2023), 1, Seite 108-130 Online-Ressource (DE-627)301516936 (DE-600)1485107-6 (DE-576)273877801 1464-5327 nnns volume:43 year:2023 number:1 pages:108-130 https://www.tandfonline.com/doi/pdf/10.1080/01441647.2022.2036864 Verlag kostenfrei https://doi.org/10.1080/01441647.2022.2036864 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_206 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 43 2023 1 108-130 26 01 0206 4292208719 x1z 17-03-23 2403 01 DE-LFER 4321101018 00 --%%-- --%%-- n --%%-- l01 12-05-23 2403 01 DE-LFER https://doi.org/10.1080/01441647.2022.2036864 26 00 DE-206 Identifying and assessing the likelihood and consequences of maritime accidents has been a key focus of research within the maritime industry. However, conventional methods utilised for maritime risk assessment have been dominated by a few methodologies each of which have recognised weaknesses. Given the growing attention that supervised machine learning and big data applications for safety assessments have been receiving in other disciplines, a comprehensive review of the academic literature on this topic in the maritime domain has been conducted. The review encapsulates the prediction of accident occurrence, accident severity, ship detentions and ship collision risk. In particular, the purpose, methods, datasets and features of such studies are compared to better understand how such an approach can be applied in practice and its relative merits. Several key challenges within these themes are also identified, such as the availability and representativeness of the datasets and methodological challenges associated with transparency, model development and results evaluation. Whilst focused within the maritime domain, many of these findings are equally relevant to other transportation topics. This work, therefore, highlights both novel applications for applying these techniques to maritime safety and key challenges that warrant further research in order to strengthen this methodological approach.
allfields_unstemmed	10.1080/01441647.2022.2036864 doi (DE-627)1839516283 (DE-599)KXP1839516283 DE-627 ger DE-627 rda eng Rawson, Andrew verfasserin aut A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis Andrew Rawson and Mario Brito 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier accidents (dpeaa)DE-206 AIS data (dpeaa)DE-206 Machine learning (dpeaa)DE-206 maritime (dpeaa)DE-206 navigation safety (dpeaa)DE-206 risk assessment (dpeaa)DE-206 Brito, Mario verfasserin aut Enthalten in Transport reviews London [u.a.] : Taylor & Francis, 1981 43(2023), 1, Seite 108-130 Online-Ressource (DE-627)301516936 (DE-600)1485107-6 (DE-576)273877801 1464-5327 nnns volume:43 year:2023 number:1 pages:108-130 https://www.tandfonline.com/doi/pdf/10.1080/01441647.2022.2036864 Verlag kostenfrei https://doi.org/10.1080/01441647.2022.2036864 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_206 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 43 2023 1 108-130 26 01 0206 4292208719 x1z 17-03-23 2403 01 DE-LFER 4321101018 00 --%%-- --%%-- n --%%-- l01 12-05-23 2403 01 DE-LFER https://doi.org/10.1080/01441647.2022.2036864 26 00 DE-206 Identifying and assessing the likelihood and consequences of maritime accidents has been a key focus of research within the maritime industry. However, conventional methods utilised for maritime risk assessment have been dominated by a few methodologies each of which have recognised weaknesses. Given the growing attention that supervised machine learning and big data applications for safety assessments have been receiving in other disciplines, a comprehensive review of the academic literature on this topic in the maritime domain has been conducted. The review encapsulates the prediction of accident occurrence, accident severity, ship detentions and ship collision risk. In particular, the purpose, methods, datasets and features of such studies are compared to better understand how such an approach can be applied in practice and its relative merits. Several key challenges within these themes are also identified, such as the availability and representativeness of the datasets and methodological challenges associated with transparency, model development and results evaluation. Whilst focused within the maritime domain, many of these findings are equally relevant to other transportation topics. This work, therefore, highlights both novel applications for applying these techniques to maritime safety and key challenges that warrant further research in order to strengthen this methodological approach.
allfieldsGer	10.1080/01441647.2022.2036864 doi (DE-627)1839516283 (DE-599)KXP1839516283 DE-627 ger DE-627 rda eng Rawson, Andrew verfasserin aut A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis Andrew Rawson and Mario Brito 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier accidents (dpeaa)DE-206 AIS data (dpeaa)DE-206 Machine learning (dpeaa)DE-206 maritime (dpeaa)DE-206 navigation safety (dpeaa)DE-206 risk assessment (dpeaa)DE-206 Brito, Mario verfasserin aut Enthalten in Transport reviews London [u.a.] : Taylor & Francis, 1981 43(2023), 1, Seite 108-130 Online-Ressource (DE-627)301516936 (DE-600)1485107-6 (DE-576)273877801 1464-5327 nnns volume:43 year:2023 number:1 pages:108-130 https://www.tandfonline.com/doi/pdf/10.1080/01441647.2022.2036864 Verlag kostenfrei https://doi.org/10.1080/01441647.2022.2036864 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_206 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 43 2023 1 108-130 26 01 0206 4292208719 x1z 17-03-23 2403 01 DE-LFER 4321101018 00 --%%-- --%%-- n --%%-- l01 12-05-23 2403 01 DE-LFER https://doi.org/10.1080/01441647.2022.2036864 26 00 DE-206 Identifying and assessing the likelihood and consequences of maritime accidents has been a key focus of research within the maritime industry. However, conventional methods utilised for maritime risk assessment have been dominated by a few methodologies each of which have recognised weaknesses. Given the growing attention that supervised machine learning and big data applications for safety assessments have been receiving in other disciplines, a comprehensive review of the academic literature on this topic in the maritime domain has been conducted. The review encapsulates the prediction of accident occurrence, accident severity, ship detentions and ship collision risk. In particular, the purpose, methods, datasets and features of such studies are compared to better understand how such an approach can be applied in practice and its relative merits. Several key challenges within these themes are also identified, such as the availability and representativeness of the datasets and methodological challenges associated with transparency, model development and results evaluation. Whilst focused within the maritime domain, many of these findings are equally relevant to other transportation topics. This work, therefore, highlights both novel applications for applying these techniques to maritime safety and key challenges that warrant further research in order to strengthen this methodological approach.
allfieldsSound	10.1080/01441647.2022.2036864 doi (DE-627)1839516283 (DE-599)KXP1839516283 DE-627 ger DE-627 rda eng Rawson, Andrew verfasserin aut A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis Andrew Rawson and Mario Brito 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier accidents (dpeaa)DE-206 AIS data (dpeaa)DE-206 Machine learning (dpeaa)DE-206 maritime (dpeaa)DE-206 navigation safety (dpeaa)DE-206 risk assessment (dpeaa)DE-206 Brito, Mario verfasserin aut Enthalten in Transport reviews London [u.a.] : Taylor & Francis, 1981 43(2023), 1, Seite 108-130 Online-Ressource (DE-627)301516936 (DE-600)1485107-6 (DE-576)273877801 1464-5327 nnns volume:43 year:2023 number:1 pages:108-130 https://www.tandfonline.com/doi/pdf/10.1080/01441647.2022.2036864 Verlag kostenfrei https://doi.org/10.1080/01441647.2022.2036864 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_206 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 43 2023 1 108-130 26 01 0206 4292208719 x1z 17-03-23 2403 01 DE-LFER 4321101018 00 --%%-- --%%-- n --%%-- l01 12-05-23 2403 01 DE-LFER https://doi.org/10.1080/01441647.2022.2036864 26 00 DE-206 Identifying and assessing the likelihood and consequences of maritime accidents has been a key focus of research within the maritime industry. However, conventional methods utilised for maritime risk assessment have been dominated by a few methodologies each of which have recognised weaknesses. Given the growing attention that supervised machine learning and big data applications for safety assessments have been receiving in other disciplines, a comprehensive review of the academic literature on this topic in the maritime domain has been conducted. The review encapsulates the prediction of accident occurrence, accident severity, ship detentions and ship collision risk. In particular, the purpose, methods, datasets and features of such studies are compared to better understand how such an approach can be applied in practice and its relative merits. Several key challenges within these themes are also identified, such as the availability and representativeness of the datasets and methodological challenges associated with transparency, model development and results evaluation. Whilst focused within the maritime domain, many of these findings are equally relevant to other transportation topics. This work, therefore, highlights both novel applications for applying these techniques to maritime safety and key challenges that warrant further research in order to strengthen this methodological approach.
language	English
source	Enthalten in Transport reviews 43(2023), 1, Seite 108-130 volume:43 year:2023 number:1 pages:108-130
sourceStr	Enthalten in Transport reviews 43(2023), 1, Seite 108-130 volume:43 year:2023 number:1 pages:108-130
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institution	findex.gbv.de
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topic_facet	accidents AIS data Machine learning maritime navigation safety risk assessment
sw_local_iln_str_mv	26: DE-206:
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container_title	Transport reviews
authorswithroles_txt_mv	Rawson, Andrew @@aut@@ Brito, Mario @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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topic_title	26 00 DE-206 Identifying and assessing the likelihood and consequences of maritime accidents has been a key focus of research within the maritime industry. However, conventional methods utilised for maritime risk assessment have been dominated by a few methodologies each of which have recognised weaknesses. Given the growing attention that supervised machine learning and big data applications for safety assessments have been receiving in other disciplines, a comprehensive review of the academic literature on this topic in the maritime domain has been conducted. The review encapsulates the prediction of accident occurrence, accident severity, ship detentions and ship collision risk. In particular, the purpose, methods, datasets and features of such studies are compared to better understand how such an approach can be applied in practice and its relative merits. Several key challenges within these themes are also identified, such as the availability and representativeness of the datasets and methodological challenges associated with transparency, model development and results evaluation. Whilst focused within the maritime domain, many of these findings are equally relevant to other transportation topics. This work, therefore, highlights both novel applications for applying these techniques to maritime safety and key challenges that warrant further research in order to strengthen this methodological approach A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis Andrew Rawson and Mario Brito accidents (dpeaa)DE-206 AIS data (dpeaa)DE-206 Machine learning (dpeaa)DE-206 maritime (dpeaa)DE-206 navigation safety (dpeaa)DE-206 risk assessment (dpeaa)DE-206
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title_short	A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis
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code="e">n</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">12-05-23</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://doi.org/10.1080/01441647.2022.2036864</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">Identifying and assessing the likelihood and consequences of maritime accidents has been a key focus of research within the maritime industry. 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A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis

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