Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data - Paefgen, Johannes; Staake, Thorsten; Fleisch, Elgar (Transportation research. Part A, Policy and practice, 2014)
•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 R2 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. 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 R2 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.