Driver Liability Assessment in Vehicle Collisions in Spain

Abstract

An accurate estimation of exposure is essential for road collision rate estimation, which is key when evaluating the impact of road safety measures. The quasi-induced exposure method was developed to estimate relative exposure for different driver groups based on its main hypothesis: the not-at-fault drivers involved in two-vehicle collisions are taken as a random sample of driver populations. Liability assignment is thus crucial in this method to identify not-at-fault drivers, but often no liability labels are given in collision records, so unsupervised analysis tools are required. To date, most researchers consider only driver and speed offences in liability assignment, but an open question is if more information could be added. To this end, in this paper, the visual clustering technique of self-organizing maps (SOM) has been applied to better understand the multivariate structure in the data, to find out the most important variables for driver liability, analyzing their influence, and to identify relevant liability patterns. The results show that alcohol/drug use could be influential on liability and further analysis is required for disability and sudden illness. More information has been used, given that a larger proportion of the data was considered. SOM thus appears as a promising tool for liability assessment.

Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019

“…Around the world, researchers are working on alternative means of elicitation, exploring new data sources, and in the Quasi-Induced Exposure (QIE) methodology, from data contained in accident databases. In the paper [81], they have developed a methodology for improving driver crash liability useful for the QIE approach. In addition, in research papers, it can be found that the age is a factor to be taken into account.…”

Identificación de responsabilidad de los conductores. Aplicación al método de exposición cuasi-inducida

“…Así, por ejemplo, el clúster 15 contiene un total de 30.729 conductores, mientras que en el clúster 23 no hay ningún conductor. Un análisis más detallado puede encontrarse en Sanjurjo- de-No et al (2021).…”

“…Por tanto, basándonos en las hipótesis planteadas, se escogió el valor de 0,25 para los casos en los que se desconocía el valor de la variable. Más información puede encontrarse en Sanjurjo- de-No et al (2021).…”

Reassessing Fitness-to-Drive in Drinker Drivers: The Role of Cognition and Personality

“…Despite the results presented here, and the importance of personality in affecting driving behaviors, self-reported personality measures should be considered with caution [ 108 ] considering their exposure to faking good, especially in forensic assessments in which they could potentially lead to paradoxical effects. Furthermore, these results may be a relevant source of knowledge for researchers dealing with both the prediction of motor vehicle-crashes and the driver’s liability in such events [ 109 ], providing information about variables that are able in detecting drinker drivers.…”