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

Johannes Paefgen, Thorsten Staake and Elgar Fleisch

Transportation Research Part A: Policy and Practice, 2014, vol. 61, issue C, 27-40

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 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.

Keywords: GPS trajectories; In-vehicle data recorder; Risk exposure; Accident research; Pay-as-you-drive insurance; Multivariate logistic regression (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (19)

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DOI: 10.1016/j.tra.2013.11.010

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