Highlights We compute differentiated mileage exposure metrics from 1600 vehicles. Metrics are used in multivariate logistic regression to predict accident involvement. After various transformations, a Nagelkerke R 2 goodness-of-fit of 0.646 is achieved. Multivariate mileage–risk relationship modeling offers novel insights. PAYD-insurance data are an important opportunity for transportation research.

    Abstract The increasing adoption of in-vehicle data recorders (IVDR) for commercial purposes such as Pay-as-you-drive (PAYD) insurance is generating new opportunities for transportation researchers. An important yet currently underrepresented theme of IVDR-based studies is the relationship between the risk of accident involvement and exposure variables that differentiate various driving conditions. Using an extensive commercial data set, we develop a methodology for the extraction of exposure metrics from location trajectories and estimate a range of multivariate logistic regression models in a case-control study design. We achieve high model fit (Nagelkerke’s R 2 0.646, Hosmer–Lemeshow significance 0.848) and gain insights into the non-linear relationship between mileage and accident risk. We validate our results with official accident statistics and outline further research opportunities. We hope this work provides a blueprint supporting a standardized conceptualization of exposure to accident risk in the transportation research community that improves the comparability of future studies on the subject.


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    Title :

    Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data


    Contributors:


    Publication date :

    2013-11-24


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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