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Applying crash data to injury claims - an investigation of determinant factors in severe motor vehicle accidents

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dc.contributor.author Shannon, Darren
dc.contributor.author Murphy, Finbarr
dc.contributor.author Mullins, Martin
dc.contributor.author Eggert, Julian
dc.date.accessioned 2020-06-03T11:51:31Z
dc.date.issued 2018
dc.identifier.citation Shannon, D;Murphy, F;Mullins, M;Eggert, J (2018) 'Applying crash data to injury claims - an investigation of determinant factors in severe motor vehicle accidents'. Accident Analysis And Prevention, 113 :244-256. en_US
dc.identifier.issn 0001-4575
dc.identifier.uri http://hdl.handle.net/10344/8885
dc.description peer-reviewed en_US
dc.description The full text of this article will not be available in ULIR until the embargo expires on the 07/03/2021
dc.description.abstract An extensive number of research studies have attempted to capture the factors that influence the severity of vehicle impacts. The high number of risks facing all traffic participants has led to a gradual increase in sophisticated data collection schemes linking crash characteristics to subsequent severity measures. This study serves as a departure from previous research by relating injuries suffered in road traffic accidents to expected trauma compensation payouts and deriving a quantitative cost function. Data from the National Highway Traffic Safety Administration's (NHTSA) Crash Injury Research (CIREN) database for the years 2005-2014 is combined with the Book of Quantum, an Irish governmental document that offers guidelines on the appropriate compensation to be awarded for injuries sustained in accidents. A multiple linear regression is carried out to identify the crash factors that significantly influence expected compensation costs and compared to ordered and multinomial logit models. The model offers encouraging results given the inherent variation expected in vehicular incidents and the subjectivity influencing compensation payout judgments, attaining an adjusted-R-2 fit of 20.6% when uninfluential factors are removed. It is found that relative speed at time of impact and dark conditions increase the expected costs, while rear-end incidents, incident sustained in van-based trucks and incidents sustained while turning result in lower expected compensations. The number of airbags available in the vehicle is also a significant factor. The scalar-outcome approach used in this research offers an alternative methodology to the discrete-outcome models that dominate traffic safety analyses. The results also raise queries on the future development of claims reserving (capital allocations earmarked for future expected claims payments) as advanced driver assistant systems (ADASs) seek to eradicate the most frequent types of crash factors upon which insurance mathematics base their assumptions. en_US
dc.language.iso eng en_US
dc.publisher Elsevier en_US
dc.relation 690772 en_US
dc.relation.ispartofseries Accident Analysis & Prevention;113, pp. 244-256
dc.relation.uri http://dx.doi.org/10.1016/j.aap.2018.01.037
dc.rights This is the author’s version of a work that was accepted for publication in Accident Analysis & Prevention. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Accident Analysis & Prevention, 113, 244-256, http://dx.doi.org/10.1016/j.aap.2018.01.037 en_US
dc.subject RTAs en_US
dc.subject linear regression en_US
dc.subject ADAS en_US
dc.subject expected compensation en_US
dc.subject costsClaims en_US
dc.subject reserving en_US
dc.title Applying crash data to injury claims - an investigation of determinant factors in severe motor vehicle accidents en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.date.updated 2020-04-29T09:13:13Z
dc.identifier.doi 10.1016/j.aap.2018.01.037
dc.contributor.sponsor ERC en_US
dc.relation.projectid 690772 en_US
dc.date.embargoEndDate 2021-03-07
dc.embargo.terms 2021-03-07 en_US
dc.rights.accessrights info:eu-repo/semantics/embargoedAccess en_US
dc.internal.rssid 2864660
dc.internal.copyrightchecked Yes
dc.identifier.journaltitle Accident Analysis And Prevention
dc.description.status peer-reviewed


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