IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i13p7705-d846598.html
   My bibliography  Save this article

A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance

Author

Listed:
  • Wei Nai

    (Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing 314051, China)

  • Zan Yang

    (Faculty of Science, Tongji Zhejiang College, Jiaxing 314051, China)

  • Yinzhen Wei

    (Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing 314051, China)

  • Jierui Sang

    (Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing 314051, China)

  • Jialu Wang

    (Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing 314051, China)

  • Zhou Wang

    (Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing 314051, China)

  • Peiyu Mo

    (Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing 314051, China)

Abstract

Vehicle insurance is a very important source of income for insurance companies, and it is closely related to the driving style performed by driving behavior. Different driving styles can better reflect the driving risk than the number of violations, claims, and other static statistic data. Subdivide the vehicle insurance market according to the personal characteristics and driving habits of the insured vehicles, and studying the personalized vehicle insurance products, will help the insurance companies to improve their income, help the drivers to change their bad driving habits, and thus help to realize the healthy development of the vehicle insurance industry. In the past 20 to 30 years, more and more insurance companies around the world have launched vehicle usage-based insurance (UBI) products based on driving style analysis. However, up to now, there are few comprehensive reports on commercial vehicle UBI products and their core driving risk assessment methods. On the basis of literature indexing on the Web of Science and other academic platforms by using the keywords involved in vehicle UBI, over 100 relevant works of literature were screened in this paper, and a detailed and comprehensive discussion on the driving style evaluation methods and the design of commercial vehicle UBI products during the past 20 to 30 years has been made, hoping to get a full understanding of the possible factors affecting driving style and the collectible data that can reflect these factors, and to get a full grasp of the developing status, challenges and future trends in vehicle insurance branch of the Internet of Vehicles (IoV) industry.

Suggested Citation

  • Wei Nai & Zan Yang & Yinzhen Wei & Jierui Sang & Jialu Wang & Zhou Wang & Peiyu Mo, 2022. "A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7705-:d:846598
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/13/7705/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/13/7705/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aaron S. Edlin & Pinar Karaca-Mandic, 2006. "The Accident Externality from Driving," Journal of Political Economy, University of Chicago Press, vol. 114(5), pages 931-955, October.
    2. Parry, Ian W. H., 2004. "Comparing alternative policies to reduce traffic accidents," Journal of Urban Economics, Elsevier, vol. 56(2), pages 346-368, September.
    3. Alma Cohen, 2005. "Asymmetric Information and Learning: Evidence from the Automobile Insurance Market," The Review of Economics and Statistics, MIT Press, vol. 87(2), pages 197-207, May.
    4. Amy Finkelstein & Kathleen McGarry, 2006. "Multiple Dimensions of Private Information: Evidence from the Long-Term Care Insurance Market," American Economic Review, American Economic Association, vol. 96(4), pages 938-958, September.
    5. Desyllas, Panos & Sako, Mari, 2013. "Profiting from business model innovation: Evidence from Pay-As-You-Drive auto insurance," Research Policy, Elsevier, vol. 42(1), pages 101-116.
    6. Philippe Donder & Jean Hindriks, 2009. "Adverse selection, moral hazard and propitious selection," Journal of Risk and Uncertainty, Springer, vol. 38(1), pages 73-86, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yiwen Zhou & Fengxiang Guo & Simin Wu & Wenyao He & Xuefei Xiong & Zheng Chen & Dingan Ni, 2022. "Safety and Economic Evaluations of Electric Public Buses Based on Driving Behavior," Sustainability, MDPI, vol. 14(17), pages 1-17, August.
    2. Tiande Mo & Yu Li & Kin-tak Lau & Chi Kin Poon & Yinghong Wu & Yang Luo, 2022. "Trends and Emerging Technologies for the Development of Electric Vehicles," Energies, MDPI, vol. 15(17), pages 1-34, August.
    3. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.
    4. Serkan Eti & Hasan Dinçer & Hasan Meral & Serhat Yüksel & Yaşar Gökalp, 2024. "Insurtech in Europe: identifying the top investment priorities for driving innovation," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-24, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arvidsson, Sara, 2010. "Reducing asymmetric information with usage-based automobile insurance," Working Papers 2010:2, Swedish National Road & Transport Research Institute (VTI), revised 03 Feb 2011.
    2. Deryugina, Tatyana, 2012. "Does Selection in Insurance Markets Always Favor Buyers?," MPRA Paper 53583, University Library of Munich, Germany.
    3. Alois Geyer & Daniela Kremslehner & Alexander Muermann, 2020. "Asymmetric Information in Automobile Insurance: Evidence From Driving Behavior," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(4), pages 969-995, December.
    4. Karl Ove Aarbu, 2017. "Asymmetric Information in the Home Insurance Market," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(1), pages 35-72, March.
    5. Peilu Zhang & Marco A. Palma, 2021. "Compulsory Versus Voluntary Insurance: An Online Experiment," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(1), pages 106-125, January.
    6. Dionne, Georges & Fombaron, Nathalie & Doherty, Neil, 2012. "Adverse selection in insurance contracting," Working Papers 12-8, HEC Montreal, Canada Research Chair in Risk Management.
    7. Kremslehner, Daniela & Muermann, Alexander, 2016. "Asymmetric information in automobile insurance: Evidence from driving behavior," CFS Working Paper Series 543, Center for Financial Studies (CFS).
    8. Gan, Li & Huang, Feng & Mayer, Adalbert, 2015. "A simple test for private information in insurance markets with heterogeneous insurance demand," Economics Letters, Elsevier, vol. 136(C), pages 197-200.
    9. Fabio Gaetano Santeramo, 2018. "Imperfect information and participation in insurance markets: evidence from Italy," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 78(2), pages 183-194, February.
    10. Alfiero, Simona & Battisti, Enrico & Ηadjielias, Elias, 2022. "Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    11. Jean Pinquet & Georges Dionne & Charles Vanasse & Mathieu Maurice, 2007. "Point-record incentives, asymmetric information and dynamic data," Working Papers hal-00243056, HAL.
    12. repec:mea:meawpa:12259 is not listed on IDEAS
    13. Ian W. H. Parry, 2005. "Is Pay-as-You-Drive Insurance a Better Way to Reduce Gasoline than Gasoline Taxes?," American Economic Review, American Economic Association, vol. 95(2), pages 288-293, May.
    14. Hanming Fang & Michael P. Keane & Dan Silverman, 2008. "Sources of Advantageous Selection: Evidence from the Medigap Insurance Market," Journal of Political Economy, University of Chicago Press, vol. 116(2), pages 303-350, April.
    15. Arvidsson, Sara, 2011. "Predictors of customer loyalty in automobile insurance - The role of private information in risky driving behavior and claim history," Working papers in Transport Economics 2011:2, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    16. Keane, Michael & Stavrunova, Olena, 2016. "Adverse selection, moral hazard and the demand for Medigap insurance," Journal of Econometrics, Elsevier, vol. 190(1), pages 62-78.
    17. Tsai, Jyh-Fa & Chu, Chih-Peng & Hu, Shou-Ren, 2015. "Road pricing for congestion and accident externalities for mixed traffic of motorcycles and automobiles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 71(C), pages 153-166.
    18. Hanming Fang & Zenan Wu, 2018. "Multidimensional private information, market structure, and insurance markets," RAND Journal of Economics, RAND Corporation, vol. 49(3), pages 751-787, September.
    19. Ma, Yu-Luen & Zhu, Xiaoyu & Hu, Xianbiao & Chiu, Yi-Chang, 2018. "The use of context-sensitive insurance telematics data in auto insurance rate making," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 243-258.
    20. Dardanoni, V & Li Donni, P, 2008. "Testing For Asymmetric Information In Insurance Markets With Unobservable Types," Health, Econometrics and Data Group (HEDG) Working Papers 08/26, HEDG, c/o Department of Economics, University of York.
    21. Sebastian Soika, 2018. "Moral Hazard and Advantageous Selection in Private Disability Insurance," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 43(1), pages 97-125, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7705-:d:846598. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.