Driver Liability Assessment in Vehicle Collisions in Spain
| Autor: | Almudena Sanjurjo-de-No, Francisco Aparicio-Izquierdo, José Mira, Blanca Arenas-Ramírez |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Multivariate statistics
Automobile Driving Computer science Health Toxicology and Mutagenesis lcsh:Medicine quasi-induced exposure Article driver liability assignment 0502 economics and business In vehicle 0501 psychology and cognitive sciences Cluster analysis 050107 human factors Estimation 050210 logistics & transportation vehicle collisions 05 social sciences Liability lcsh:R Public Health Environmental and Occupational Health pattern identification Accidents Traffic Collision Self-Organizing Maps (SOM) Risk analysis (engineering) Spain Key (cryptography) Analysis tools road safety human activities |
| Zdroj: | International Journal of Environmental Research and Public Health International Journal of Environmental Research and Public Health, Vol 18, Iss 1475, p 1475 (2021) Volume 18 Issue 4 |
| ISSN: | 1660-4601 1661-7827 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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