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

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.