Citation

Capanni L, Berzi L, Barbieri R, Capitani R. J. Transp. Saf. Secur. 2018; 10(5): 429-454.

Copyright

(Copyright © 2018, Southeastern Transportation Center, and Beijing Jiaotong University, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19439962.2017.1294227

PMID

unavailable

Abstract

Road accident risk assessment is a complex topic both due to the large number of factors determining it and to the difficulties to collect data. In addition, most exposure factors influencing crash probability, such as environment and driver characteristics, are dependent from each other, so that it is not intuitive to devise a cause-effect scenario. The use of telematics devices, recently spreading among insurance and rental companies, provides new chances to collect exposure data, to define interpretive models of accident risk and to explain variables relationships. Using GPS data available through a Long Term Rental company, we studied a sample of 900 vehicles. We aggregated raw data (e.g. road type covered, time, speed) in exposure metrics and we organized them in a relational database. We built a number of multivariate logistic regression models, adopting a strategy to progressively refine them.We obtained a relatively high model fits (up to pseudo-R2 0.301, Hosmer-Lemeshow p-value 0.206) acquiring insights about the nonlinear association between explanatory variables and their outcomes. Interactions between variables were also examined. The results are, in general, in accordance with similar studies; regarding certain observed discrepancies, a discussion is provided to explain their origin, starting from the differences in associating predictors, outcome and interaction variables.

Language: en