{"id":{"repo_id":"aalto","oai_identifier":"oai:aaltodoc.aalto.fi:123456789/15104"},"canonical_url":"https://search.dev.ndltd.org/etd/aalto/oai:aaltodoc.aalto.fi:123456789/15104","repository":{"repo_id":"aalto","name":"Aalto University","base_url":"https://aaltodoc.aalto.fi/server/oai/request"},"display":{"title":"Bayesian network modeling of potential patterns in maritime safety performance","abstract":"Although major maritime accidents occur rather rarely, they can produce severe consequences. Safety management aims at preventing such accidents. For monitoring and improving safety, safety management requires knowledge on the safety performance. Information on various variables which are potentially related to safety and a description of how the variables interact, or which types of patterns the interactions constitute, could be beneficial for increasing that knowledge. However, these patterns might not be apparent, as maritime traffic and its safety are complicated systems. Utilizing Bayesian network modeling approach, this thesis explores potential patterns between variables related to maritime safety. The decision-makers can then exploit this information as a starting point for analyses of the mechanisms which have generated these patterns and of what the patterns tell about safety. The thesis models safety-performance patterns from different, complementing angles. The work begins with examining the feasibility of maritime accident causation pattern models for the exploration of safety performance. This includes analyzing an existing causal collision model and assessing the feasibility of accident and incident data for collision and grounding cause pattern learning. The focus then shifts to patterns present in multiple safety indicator data, before the analysis is extended to safety management patterns and safety management dependencies with safety performance. While causal pattern modeling is found questionable, Port State Control inspections have potential to act as a valuable data source for safety performance information. However, the inspections in Finnish ports have resulted in few deficiencies and thus the data contains only weak patterns. It might be worth evaluating whether the Port State Control could be developed so that the inspections would produce data on more detailed safety performance differences between different ships. On the other hand, maritime safety management seems to be a rather tightly coupled system with several properly functioning subareas but inadequate as a whole. Regarding the application of Bayesian networks for the pattern modeling problem, the thesis concludes that with their capability to express uncertain, rather complex dependencies and to combine data with expert knowledge, Bayesian networks offer an attractive tool for such a task. As the amount of collected and shared data is slowly increasing within the maritime community, the Bayesian network models can be easily updated with new information, and thus their quality and worth for decision-support could be improved.","abstract_html":"Although major maritime accidents occur rather rarely, they can produce severe consequences. Safety management aims at preventing such accidents. For monitoring and improving safety, safety management requires knowledge on the safety performance. Information on various variables which are potentially related to safety and a description of how the variables interact, or which types of patterns the interactions constitute, could be beneficial for increasing that knowledge. However, these patterns might not be apparent, as maritime traffic and its safety are complicated systems. Utilizing Bayesian network modeling approach, this thesis explores potential patterns between variables related to maritime safety. The decision-makers can then exploit this information as a starting point for analyses of the mechanisms which have generated these patterns and of what the patterns tell about safety. The thesis models safety-performance patterns from different, complementing angles. The work begins with examining the feasibility of maritime accident causation pattern models for the exploration of safety performance. This includes analyzing an existing causal collision model and assessing the feasibility of accident and incident data for collision and grounding cause pattern learning. The focus then shifts to patterns present in multiple safety indicator data, before the analysis is extended to safety management patterns and safety management dependencies with safety performance. While causal pattern modeling is found questionable, Port State Control inspections have potential to act as a valuable data source for safety performance information. However, the inspections in Finnish ports have resulted in few deficiencies and thus the data contains only weak patterns. It might be worth evaluating whether the Port State Control could be developed so that the inspections would produce data on more detailed safety performance differences between different ships. On the other hand, maritime safety management seems to be a rather tightly coupled system with several properly functioning subareas but inadequate as a whole. Regarding the application of Bayesian networks for the pattern modeling problem, the thesis concludes that with their capability to express uncertain, rather complex dependencies and to combine data with expert knowledge, Bayesian networks offer an attractive tool for such a task. As the amount of collected and shared data is slowly increasing within the maritime community, the Bayesian network models can be easily updated with new information, and thus their quality and worth for decision-support could be improved.","abstract_has_math":false,"creators":["Hänninen, Maria"],"institution":"Aalto University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Sovelletun mekaniikan laitos","school":null,"contributors":["Aalto-yliopisto","Aalto University"],"advisors":["Kujala, Pentti, Prof., Aalto University, Department of Applied Mechanics, Finland","Kuikka, Sakari, Prof., University of Helsinki, Finland"],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-08-21T16:42:07Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://aaltodoc.aalto.fi/handle/123456789/15104","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"source_record":{"url":"https://aaltodoc.aalto.fi/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Aaaltodoc.aalto.fi%3A123456789%2F15104","prefix":"dim"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Aalto-yliopisto","Aalto University"]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Kujala, Pentti, Prof., Aalto University, Department of Applied Mechanics, Finland","Kuikka, Sakari, Prof., University of Helsinki, Finland"]},{"key":"dc:contributor.department","label":"Department","values":["Sovelletun mekaniikan laitos","Department of Applied Mechanics"]},{"key":"dc:contributor.supervisor","label":"Supervisor","values":["Kujala, Pentti, Prof., Aalto University, Department of Applied Mechanics, Finland"]},{"key":"dc:creator","label":"Author","values":["Hänninen, Maria"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-02-04T10:00:31Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-02-04T10:00:31Z"]},{"key":"dc:date.issued","label":"Date","values":["2015"]},{"key":"dc:publisher","label":"Institution","values":["Aalto University","Aalto-yliopisto"]},{"key":"dc:type","label":"Dc Type","values":["G5 Artikkeliväitöskirja"]},{"key":"dc:type.dcmitype","label":"Dc Type Dcmitype","values":["text"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://aaltodoc.aalto.fi/handle/123456789/15104"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Although major maritime accidents occur rather rarely, they can produce severe consequences. Safety management aims at preventing such accidents. For monitoring and improving safety, safety management requires knowledge on the safety performance. Information on various variables which are potentially related to safety and a description of how the variables interact, or which types of patterns the interactions constitute, could be beneficial for increasing that knowledge. However, these patterns might not be apparent, as maritime traffic and its safety are complicated systems. Utilizing Bayesian network modeling approach, this thesis explores potential patterns between variables related to maritime safety. The decision-makers can then exploit this information as a starting point for analyses of the mechanisms which have generated these patterns and of what the patterns tell about safety. The thesis models safety-performance patterns from different, complementing angles. The work begins with examining the feasibility of maritime accident causation pattern models for the exploration of safety performance. This includes analyzing an existing causal collision model and assessing the feasibility of accident and incident data for collision and grounding cause pattern learning. The focus then shifts to patterns present in multiple safety indicator data, before the analysis is extended to safety management patterns and safety management dependencies with safety performance. While causal pattern modeling is found questionable, Port State Control inspections have potential to act as a valuable data source for safety performance information. However, the inspections in Finnish ports have resulted in few deficiencies and thus the data contains only weak patterns. It might be worth evaluating whether the Port State Control could be developed so that the inspections would produce data on more detailed safety performance differences between different ships. On the other hand, maritime safety management seems to be a rather tightly coupled system with several properly functioning subareas but inadequate as a whole. Regarding the application of Bayesian networks for the pattern modeling problem, the thesis concludes that with their capability to express uncertain, rather complex dependencies and to combine data with expert knowledge, Bayesian networks offer an attractive tool for such a task. As the amount of collected and shared data is slowly increasing within the maritime community, the Bayesian network models can be easily updated with new information, and thus their quality and worth for decision-support could be improved.","Merkittävät laivaonnettomuudet ovat melko harvinaisia, mutta niiden seuraukset voivat olla vakavia. Turvallisuusjohtamisella pyritään estämään tämänkaltaiset onnettomuudet. Jotta turvallisuusjohtaminen pystyy seuraamaan ja parantamaan turvallisuutta, se tarvitsee tietoa turvallisuuden tilasta. Tilan määrittämiseksi voisi olla hyödyllistä tarkastella turvallisuuteen liittyviä tekijöitä ja niiden välisiä riippuvuuksia. Ne eivät kuitenkaan välttämättä ole ilmeisiä, sillä merenkulku ja sen turvallisuus ovat monimutkaisia kokonaisuuksia. Työssä tutkitaan meriturvallisuuteen liittyvien tekijöiden välisiä riippuvuuksia Bayes-verkkomallinnuksen keinoin. Päätöksentekijät voivat hyödyntää havaittuja riippuvuuksia analysoidessaan mahdollisia tekijöitä kytköksien taustalla ja sitä, mitä riippuvuudet kertovat turvallisuuden tilasta. Riippuvuuksia mallinnetaan useasta toisiaan täydentävästä näkökulmasta. Aluksi tutkitaan onnettomuuksien syysuhteisten riippuvuuksien mallintamista turvallisuuden tilan arvioinnissa kahdella eri lähestymistavalla. Sen jälkeen etsitään riippuvuuksia aineistosta, joka sisältää useita turvallisuutta mittaavia indikaattoreita. Lopuksi analyysi laajennetaan käsittämään turvallisuusjohtamisen sisäiset sekä sen ja turvallisuuden väliset riippuvuudet. Työssä havaitaan, että syysuhteisten riippuvuuksien mallintaminen on kyseenalaista. Sen sijaan satamavaltiotarkastukset voisivat sopia tiedonlähteeksi turvallisuuden tilan arviointiin, mutta valitettavasti Suomen satamissa kerätty tarkastusaineisto sisältää vain vähän havaittuja puutteita. Siksi erityyppisten puutteiden väliset riippuvuudet eivät ole kovin selkeitä. Olisikin syytä tutkia, voiko tarkastuksia kehittää niin, että ne tuottaisivat tietoa nykyistä pienemmistä turvallisuuseroista alusten välillä. Toisaalta huomataan, että merenkulun turvallisuusjohtamisen osa-alueet ovat tiiviisti linkittyneitä. Useat niistä toimivat asianmukaisesti, mutta kokonaisuutena turvallisuusjohtaminen ei vielä ole riittävän hyvällä tasolla. Tarkasteltaessa Bayes-verkkojen soveltuvuutta meriturvallisuuden mallinnukseen todetaan, että verkkojen kyky kuvata epävarmuutta sisältäviä, melko monimutkaisia vuorovaikutuksia ja yhdistää dataa asiantuntijatietämykseen tarjoaa houkuttelevan työkalun turvallisuusriippuvuuksien kuvaamiseen. Merenkulussa kerättävän ja jaettavan tiedon määrä on hitaasti kasvamassa, ja Bayes-verkkomalleja voidaan vaivatta päivittää, kun lisää aineistoa on saatavilla. Näin voidaan parantaa mallien laatua ja hyödyllisyyttä päätöksenteon tukena."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Bayesian network modeling of potential patterns in maritime safety performance","Merenkulun turvallisuustekijöiden riippuvuuksien mallinnus Bayes-verkoilla"]}]}],"canonical_facts":{"dc:contributor":["Aalto-yliopisto","Aalto University"],"dc:contributor.advisor":["Kujala, Pentti, Prof., Aalto University, Department of Applied Mechanics, Finland","Kuikka, Sakari, Prof., University of Helsinki, Finland"],"dc:contributor.department":["Sovelletun mekaniikan laitos","Department of Applied Mechanics"],"dc:contributor.supervisor":["Kujala, Pentti, Prof., Aalto University, Department of Applied Mechanics, Finland"],"dc:creator":["Hänninen, Maria"],"dc:date.accessioned":["2015-02-04T10:00:31Z"],"dc:date.available":["2015-02-04T10:00:31Z"],"dc:date.issued":["2015"],"dc:description.abstract":["Although major maritime accidents occur rather rarely, they can produce severe consequences. Safety management aims at preventing such accidents. For monitoring and improving safety, safety management requires knowledge on the safety performance. Information on various variables which are potentially related to safety and a description of how the variables interact, or which types of patterns the interactions constitute, could be beneficial for increasing that knowledge. However, these patterns might not be apparent, as maritime traffic and its safety are complicated systems. Utilizing Bayesian network modeling approach, this thesis explores potential patterns between variables related to maritime safety. The decision-makers can then exploit this information as a starting point for analyses of the mechanisms which have generated these patterns and of what the patterns tell about safety. The thesis models safety-performance patterns from different, complementing angles. The work begins with examining the feasibility of maritime accident causation pattern models for the exploration of safety performance. This includes analyzing an existing causal collision model and assessing the feasibility of accident and incident data for collision and grounding cause pattern learning. The focus then shifts to patterns present in multiple safety indicator data, before the analysis is extended to safety management patterns and safety management dependencies with safety performance. While causal pattern modeling is found questionable, Port State Control inspections have potential to act as a valuable data source for safety performance information. However, the inspections in Finnish ports have resulted in few deficiencies and thus the data contains only weak patterns. It might be worth evaluating whether the Port State Control could be developed so that the inspections would produce data on more detailed safety performance differences between different ships. On the other hand, maritime safety management seems to be a rather tightly coupled system with several properly functioning subareas but inadequate as a whole. Regarding the application of Bayesian networks for the pattern modeling problem, the thesis concludes that with their capability to express uncertain, rather complex dependencies and to combine data with expert knowledge, Bayesian networks offer an attractive tool for such a task. As the amount of collected and shared data is slowly increasing within the maritime community, the Bayesian network models can be easily updated with new information, and thus their quality and worth for decision-support could be improved.","Merkittävät laivaonnettomuudet ovat melko harvinaisia, mutta niiden seuraukset voivat olla vakavia. Turvallisuusjohtamisella pyritään estämään tämänkaltaiset onnettomuudet. Jotta turvallisuusjohtaminen pystyy seuraamaan ja parantamaan turvallisuutta, se tarvitsee tietoa turvallisuuden tilasta. Tilan määrittämiseksi voisi olla hyödyllistä tarkastella turvallisuuteen liittyviä tekijöitä ja niiden välisiä riippuvuuksia. Ne eivät kuitenkaan välttämättä ole ilmeisiä, sillä merenkulku ja sen turvallisuus ovat monimutkaisia kokonaisuuksia. Työssä tutkitaan meriturvallisuuteen liittyvien tekijöiden välisiä riippuvuuksia Bayes-verkkomallinnuksen keinoin. Päätöksentekijät voivat hyödyntää havaittuja riippuvuuksia analysoidessaan mahdollisia tekijöitä kytköksien taustalla ja sitä, mitä riippuvuudet kertovat turvallisuuden tilasta. Riippuvuuksia mallinnetaan useasta toisiaan täydentävästä näkökulmasta. Aluksi tutkitaan onnettomuuksien syysuhteisten riippuvuuksien mallintamista turvallisuuden tilan arvioinnissa kahdella eri lähestymistavalla. Sen jälkeen etsitään riippuvuuksia aineistosta, joka sisältää useita turvallisuutta mittaavia indikaattoreita. Lopuksi analyysi laajennetaan käsittämään turvallisuusjohtamisen sisäiset sekä sen ja turvallisuuden väliset riippuvuudet. Työssä havaitaan, että syysuhteisten riippuvuuksien mallintaminen on kyseenalaista. Sen sijaan satamavaltiotarkastukset voisivat sopia tiedonlähteeksi turvallisuuden tilan arviointiin, mutta valitettavasti Suomen satamissa kerätty tarkastusaineisto sisältää vain vähän havaittuja puutteita. Siksi erityyppisten puutteiden väliset riippuvuudet eivät ole kovin selkeitä. Olisikin syytä tutkia, voiko tarkastuksia kehittää niin, että ne tuottaisivat tietoa nykyistä pienemmistä turvallisuuseroista alusten välillä. Toisaalta huomataan, että merenkulun turvallisuusjohtamisen osa-alueet ovat tiiviisti linkittyneitä. Useat niistä toimivat asianmukaisesti, mutta kokonaisuutena turvallisuusjohtaminen ei vielä ole riittävän hyvällä tasolla. Tarkasteltaessa Bayes-verkkojen soveltuvuutta meriturvallisuuden mallinnukseen todetaan, että verkkojen kyky kuvata epävarmuutta sisältäviä, melko monimutkaisia vuorovaikutuksia ja yhdistää dataa asiantuntijatietämykseen tarjoaa houkuttelevan työkalun turvallisuusriippuvuuksien kuvaamiseen. Merenkulussa kerättävän ja jaettavan tiedon määrä on hitaasti kasvamassa, ja Bayes-verkkomalleja voidaan vaivatta päivittää, kun lisää aineistoa on saatavilla. Näin voidaan parantaa mallien laatua ja hyödyllisyyttä päätöksenteon tukena."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://aaltodoc.aalto.fi/handle/123456789/15104"],"dc:language.iso":["en"],"dc:publisher":["Aalto University","Aalto-yliopisto"],"dc:title":["Bayesian network modeling of potential patterns in maritime safety performance","Merenkulun turvallisuustekijöiden riippuvuuksien mallinnus Bayes-verkoilla"],"dc:type":["G5 Artikkeliväitöskirja"],"dc:type.dcmitype":["text"]},"updated_at":"2026-08-21T16:42:07Z"}