Driver Liability Assessment in Vehicle Collisions in Spain

International Journal of Environmental Research and Public Health

Blanca Arenas-Ramírez¹  Francisco Aparicio-Izquierdo¹  Almudena Sanjurjo-de-No¹  José Mira² 

[1] Instituto Universitario de Investigación del Automóvil Francisco Aparicio Izquierdo (INSIA-UPM), Escuela Técnica Superior de Ingenieros Industriales (ETSII), Universidad Politécnica de Madrid (UPM), 28006 Madrid, Spain;Statistics Department, Escuela Técnica Superior de Ingenieros Industriales (ETSII), Universidad Politécnica de Madrid (UPM), 28006 Madrid, Spain;

关键词: road safety;    vehicle collisions;    pattern identification;    driver liability assignment;    Self-Organizing Maps (SOM);    quasi-induced exposure;   

DOI  :  10.3390/ijerph18041475

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown