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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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    References listed on IDEAS

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    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Jacob Azaare & Zhao Wu & Bright Nana Kwame Ahia, 2022. "Exploring the Effects of Classical Auto Insurance Rating Variables on Premium in ARDL: Is the high Policyholders’ Premium in Ghana Justified?," SAGE Open, , vol. 12(4), pages 21582440221, October.
    9. 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.
    10. 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.
    11. Toledo, Galit & Shiftan, Yoram, 2016. "Can feedback from in-vehicle data recorders improve driver behavior and reduce fuel consumption?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 194-204.
    12. 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.
    13. 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.
    14. 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.
    15. Guangyuan Gao & Mario V. Wüthrich, 2019. "Convolutional Neural Network Classification of Telematics Car Driving Data," Risks, MDPI, vol. 7(1), pages 1-18, January.
    16. 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.
    17. 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.
    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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