Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data
•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 no...
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| Published in | Transportation research. Part A, Policy and practice Vol. 61; pp. 27 - 40 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.03.2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0965-8564 1879-2375 |
| DOI | 10.1016/j.tra.2013.11.010 |
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| Abstract | •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. |
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| AbstractList | 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. •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. |
| Author | Paefgen, Johannes Fleisch, Elgar Staake, Thorsten |
| Author_xml | – sequence: 1 givenname: Johannes surname: Paefgen fullname: Paefgen, Johannes email: johannes.paefgen@outlook.com organization: Operations Management, University of St. Gallen, Dufourstrasse 40a, 9000 St. Gallen, Switzerland – sequence: 2 givenname: Thorsten surname: Staake fullname: Staake, Thorsten organization: MIS and Energy-Efficient Systems, University of Bamberg, An der Weberei 5, 96047 Bamberg, Germany – sequence: 3 givenname: Elgar surname: Fleisch fullname: Fleisch, Elgar organization: Operations Management, University of St. Gallen, Dufourstrasse 40a, 9000 St. Gallen, Switzerland |
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| Snippet | •We compute differentiated mileage exposure metrics from 1600 vehicles.•Metrics are used in multivariate logistic regression to predict accident... The increasing adoption of in-vehicle data recorders (IVDR) for commercial purposes such as Pay-as-you-drive (PAYD) insurance is generating new opportunities... |
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| SubjectTerms | Accident analysis Accident research Accidents Exposure GPS trajectories In-vehicle data recorder Insurance Multivariate logistic regression Pay-as-you-drive insurance Regression Risk Risk exposure Statistics Transportation |
| Title | Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data |
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