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

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 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.

Factores relacionados con costos por atención médica de siniestros de tránsito en Bucaramanga, Colombia

“…También se debe tener en cuenta que los costos generados pueden ser inferiores, dado que los recursos humanos y tecnológicos para la atención en salud pueden ser menores que los de países desarrollados. De otra parte, sería interesante en estudios futuros vincular variables, como ingesta de alcohol, condiciones de las vías donde ocurrió el siniestro de tránsito, uso de elementos de proyección personal, y analizar los costos indirectos como la pérdida de la capacidad de producción por las secuelas dejadas por el accidente, de esta manera se podría brindar una proyección más amplia para las asignaciones presupuestales a estos eventos y las tarifas previstas por las aseguradoras para atender esta situación, tal como nos plantean otros estudios 19,[26][27][28] ; además, desglosar los costos de atención medica directa por profesionales, medicamentos, transporte, ayudas diagnósticas y dispositivos 15,19 . Adicionalmente, vale la pena indagar respecto de la calidad de las motocicletas, de los elementos de protección personal y el uso adecuado de los mismos.…”

Regulatory and Technical Constraints: An Overview of the Technical Possibilities and Regulatory Limitations of Vehicle Telematic Data

“…Aside from a reduction in premium price, policyholders can expect economic benefits (fuel efficiency, and vehicle condition), safety benefits (feedback on driving behavior, and reduced accident risk) and security benefits (recovery of stolen vehicle) [55,56]. For insurers, more accurate pricing and enhanced fraud prevention and detection capabilities are a significant advantage of using UBI schemes [55,57]. Despite this business appetite, there are regulatory and technical constraints that hamper the implementation of this service.…”

Speed related variables for crash injury risk analysis: what has been used?

“…Similar to Conroy et al [43], the approach to determine the crash kinematics was also based on the equivalent barrier impact speed, which was determined using the crash information and vehicle event data records (EDR) [36]. Later, to investigate the determinant factors in severe motor vehicle accidents, Shannon et al [63] accounted for the speed at the time of impact using the BES, which was available from the CIREN database, estimated by technicians who examined both vehicles and scene data. Shannon et al [63] concluded that the vehicle's relative speed at the time of impact significantly influenced the level of injuries sustained.…”

“…Later, to investigate the determinant factors in severe motor vehicle accidents, Shannon et al [63] accounted for the speed at the time of impact using the BES, which was available from the CIREN database, estimated by technicians who examined both vehicles and scene data. Shannon et al [63] concluded that the vehicle's relative speed at the time of impact significantly influenced the level of injuries sustained. It is worth mentioning that despite there being six late publications (articles published in 2018), none of these studies included delta-V.…”