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

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  • Paefgen, Johannes
  • Staake, Thorsten
  • Fleisch, Elgar

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.

Suggested Citation

  • Paefgen, Johannes & Staake, Thorsten & Fleisch, Elgar, 2014. "Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 27-40.
    • Handle: RePEc:eee:transa:v:61:y:2014:i:c:p:27-40
    DOI: 10.1016/j.tra.2013.11.010
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    3. Meng, Shengwang & Gao, Yaqian & Huang, Yifan, 2022. "Actuarial intelligence in auto insurance: Claim frequency modeling with driving behavior features and improved boosted trees," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 115-127.
    4. Montserrat Guillen & Ana M. Pérez-Marín & Manuela Alcañiz, 2020. "Risk reference charts for speeding based on telematics information," IREA Working Papers 202003, University of Barcelona, Research Institute of Applied Economics, revised Apr 2020.
    5. Yi‐Jen (Ian) Ho & Siyuan Liu & Jingchuan Pu & Dian Zhang, 2022. "Is it all about you or your driving? Designing IoT‐enabled risk assessments," Production and Operations Management, Production and Operations Management Society, vol. 31(11), pages 4205-4222, November.
    6. Francis Duval & Jean‐Philippe Boucher & Mathieu Pigeon, 2023. "Enhancing claim classification with feature extraction from anomaly‐detection‐derived routine and peculiarity profiles," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 90(2), pages 421-458, June.
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    8. Donatella Porrini & Giulio Fusco & Cosimo Magazzino, 2020. "Black boxes and market efficiency: the effect on premiums in the Italian motor-vehicle insurance market," European Journal of Law and Economics, Springer, vol. 49(3), pages 455-472, June.
    9. Hsu, Yung-Ching & Shiu, Yung-Ming & Chou, Pai-Lung & Chen, Yen-Ming J., 2015. "Vehicle insurance and the risk of road traffic accidents," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 201-209.
    10. Jean-Philippe Boucher & Steven Côté & Montserrat Guillen, 2017. "Exposure as Duration and Distance in Telematics Motor Insurance Using Generalized Additive Models," Risks, MDPI, vol. 5(4), pages 1-23, September.
    11. Guadalupe González-Sánchez & María Isabel Olmo-Sánchez & Elvira Maeso-González & Mario Gutiérrez-Bedmar & Antonio García-Rodríguez, 2021. "Needs for International Benchmarking of Road Safety Management Based on Mobility Exposure Measures and Risk Patterns," IJERPH, MDPI, vol. 18(23), pages 1-13, December.
    12. Mercedes Ayuso & Montserrat Guillen & Jens Perch Nielsen, 2019. "Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data," Transportation, Springer, vol. 46(3), pages 735-752, June.
    13. Guangyuan Gao & Mario V. Wüthrich, 2019. "Convolutional Neural Network Classification of Telematics Car Driving Data," Risks, MDPI, vol. 7(1), pages 1-18, January.
    14. Ana M. Pérez-Marín & Montserrat Guillen & Manuela Alcañiz & Lluís Bermúdez, 2019. "Quantile Regression with Telematics Information to Assess the Risk of Driving above the Posted Speed Limit," Risks, MDPI, vol. 7(3), pages 1-11, July.
    15. Vukina, Tomislav & Nestić, Danijel, 2015. "Do people drive safer when accidents are more expensive: Testing for moral hazard in experience rating schemes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 71(C), pages 46-58.
    16. Omid Ghaffarpasand & Mark Burke & Louisa K. Osei & Helen Ursell & Sam Chapman & Francis D. Pope, 2022. "Vehicle Telematics for Safer, Cleaner and More Sustainable Urban Transport: A Review," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    17. Montserrat Guillen & Ana M. Pérez-Marín, 2018. "“The transition towards semi-autonomous vehicle insurance: the contribution of usage-based data”," IREA Working Papers 201811, University of Barcelona, Research Institute of Applied Economics, revised May 2018.
    18. Bian, Yiyang & Yang, Chen & Zhao, J. Leon & Liang, Liang, 2018. "Good drivers pay less: A study of usage-based vehicle insurance models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 20-34.
    19. Gao, Guangyuan & Wüthrich, Mario V. & Yang, Hanfang, 2019. "Evaluation of driving risk at different speeds," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 108-119.

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