Advertisement

Soft Computing

, Volume 23, Issue 9, pp 2863–2875 | Cite as

A “pay-how-you-drive” car insurance approach through cluster analysis

  • Maria Francesca Carfora
  • Fabio Martinelli
  • Francesco MercaldoEmail author
  • Vittoria Nardone
  • Albina Orlando
  • Antonella Santone
  • Gigliola Vaglini
Focus

Abstract

As discussed in the recent literature, several innovative car insurance concepts are proposed in order to gain advantages both for insurance companies and for drivers. In this context, the “pay-how-you-drive” paradigm is emerging, but it is not thoroughly discussed and much less implemented. In this paper, we propose an approach in order to identify the driver behavior exploring the usage of unsupervised machine learning techniques. A real-world case study is performed to evaluate the effectiveness of the proposed solution. Furthermore, we discuss how the proposed model can be adopted as risk indicator for car insurance companies.

Keywords

Insurance Risk analysis OBD CAN Cluster analysis Machine learning 

Notes

Acknowledgements

This work has been partially supported by H2020 EU-funded projects NeCS and C3ISP and EIT-Digital Project HII and PRIN “Governing Adaptive and Unplanned Systems of Systems” and the EU project CyberSure 734815.

Compliance with ethical standards

Conflict of interest

All authors declare that there is no conflict of interest

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

This article does not contain any studies with animals performed by any of the authors.

Informed consent was obtained from all individual participants included in the study.

References

  1. Arthur D, Manthey B, Röglin H (2009) k-means has polynomial smoothed complexity. In: 50th Annual IEEE symposium on foundations of computer science, 2009. FOCS’09. IEEE pp 405–414Google Scholar
  2. Bernardi ML, Cimitile M, Martinelli F, Mercaldo F (2018) Driver and path detection through time-series classification. J Adv Transport 2018:1–20CrossRefGoogle Scholar
  3. Booth P, Haberman S, Chadburn R, James D, Khorasanee Z, Plumb RH, Rickayzen B (2004) Modern actuarial theory and practice. Chapman and Hall/CRC, Baca RatonzbMATHGoogle Scholar
  4. Boquete L, Rodríguez-Ascariz JM, Barea R, Cantos J, Miguel-Jiménez JM, Ortega S (2010) Data acquisition, analysis and transmission platform for a pay-as-you-drive system. Sensors 10(6):5395–5408CrossRefGoogle Scholar
  5. Campbell M (1986) An integrated system for estimating the risk premium of individual car models in motor insurance. ASTIN Bull J IAA 16(2):165–183CrossRefGoogle Scholar
  6. Choi S, Kim J, Kwak D, Angkititrakul P, Hansen JH (2007) Analysis and classification of driver behavior using in-vehicle can-bus information. In: Biennial workshop on DSP for in-vehicle and mobile systems, pp 17–19Google Scholar
  7. Desyllas P, Sako M (2013) Profiting from business model innovation: evidence from pay-as-you-drive auto insurance. Res Policy 42(1):101–116CrossRefGoogle Scholar
  8. Enev M, Takakuwa A, Koscher K, Kohno T (2016) Automobile driver fingerprinting. Proc Priv Enhanc Technol 2016(1):34–50CrossRefGoogle Scholar
  9. Fisher DH (1987) Knowledge acquisition via incremental conceptual clustering. Mach Learn 2(2):139–172Google Scholar
  10. Gennari JH, Langley P, Fisher D (1989) Models of incremental concept formation. Artif Intell 40(1–3):11–61CrossRefGoogle Scholar
  11. Har-Peled S, Kushal A (2007) Smaller coresets for k-median and k-means clustering. Discrete Comput Geom 37(1):3–19MathSciNetCrossRefzbMATHGoogle Scholar
  12. Hochbaum DS, Shmoys DB (1985) A best possible heuristic for the k-center problem. Math Oper Res 10(2):180–184MathSciNetCrossRefzbMATHGoogle Scholar
  13. Jain AK (2010) Data clustering: 50 years beyond k-means. Pattern Recognit Lett 31(8):651–666CrossRefGoogle Scholar
  14. Kantor S, Stárek T (2014) Design of algorithms for payment telematics systems evaluating driver’s driving style. Trans Transp Sci 7(1):9CrossRefGoogle Scholar
  15. Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis, vol 344. Wiley, HobokenzbMATHGoogle Scholar
  16. Kedar-Dongarkar G, Das M (2012) Driver classification for optimization of energy usage in a vehicle. Proc Comput Sci 8:388–393CrossRefGoogle Scholar
  17. Kwak BI, Woo J, Kim HK (2016) Know your master: Driver profiling-based anti-theft method. In: PST 2016Google Scholar
  18. MacQueen J et al (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol. 1, no. 14. Oakland, CA, USA., pp 281–297Google Scholar
  19. Marotta A, Martinelli F, Nanni S, Orlando A, Yautsiukhin A (2017) Cyber-insurance survey. Comput Sci Rev 24:35CrossRefGoogle Scholar
  20. Martinelli F, Mercaldo F, Nardone V, Santone A (2017) Car hacking identification through fuzzy logic algorithms. In: IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEEGoogle Scholar
  21. Martinelli F, Mercaldo F, Orlando A, Nardone V, Santone A, Sangaiah AK (2018) Human behavior characterization for driving style recognition in vehicle system. Computers & Electrical Engineering. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0045790617329531
  22. McCallum A, Nigam K, Ungar LH (2000) Efficient clustering of high-dimensional data sets with application to reference matching. In: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 169–178Google Scholar
  23. Mehar A, Chandra S, Velmurugan S (2013) Speed and acceleration characteristics of different types of vehicles on multi-lane highways. Eur Transp 55:1825–3997Google Scholar
  24. Meng X, Lee KK, Xu Y (2006) Human driving behavior recognition based on hidden markov models. In: IEEE international conference on robotics and biomimetics, 2006. ROBIO’06. IEEE, pp 274–279Google Scholar
  25. Miyajima C, Nishiwaki Y, Ozawa K, Wakita T, Itou K, Takeda K, Itakura F (2007) Driver modeling based on driving behavior and its evaluation in driver identification. Proc IEEE 95(2):427–437CrossRefzbMATHGoogle Scholar
  26. Nishiwaki Y, Ozawa K, Wakita T, Miyajima C, Itou K, Takeda K (2007) Driver identification based on spectral analysis of driving behavioral signals. In: Advances for in-vehicle and mobile systems. Springer, pp 25–34Google Scholar
  27. Tselentis DI, Yannis G, Vlahogianni EI (2016) Innovative insurance schemes: pay as/how you drive. Transp Res Proc 14:362–371CrossRefGoogle Scholar
  28. Van Ly M, Martin S, Trivedi MM (2013) Driver classification and driving style recognition using inertial sensors. In: Intelligent vehicles symposium (IV), 2013 IEEE. IEEE, pp 1040–1045Google Scholar
  29. Wakita T, Ozawa K, Miyajima C, Igarashi K, Katunobu I, Takeda K, Itakura F (2006) Driver identification using driving behavior signals. IEICE Trans Inf Syst 89(3):1188–1194CrossRefGoogle Scholar
  30. Wang J, Dixon K, Li H, Ogle J (2004) Normal acceleration behavior of passenger vehicles starting from rest at all-way stop-controlled intersections. Transport Res Rec J Transport Res Board 1883:158–166CrossRefGoogle Scholar
  31. Zhang X, Zhao X, Rong J (2014) A study of individual characteristics of driving behavior based on hidden markov model. Sensors Transducers 167(3):194Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Istituto per le Applicazioni del Calcolo “M. Picone”Consiglio Nazionale delle RicercheNaplesItaly
  2. 2.Istituto di Informatica e TelematicaConsiglio Nazionale delle RicerchePisaItaly
  3. 3.Department of EngineeringUniversity of SannioBeneventoItaly
  4. 4.Department of Bioscience and TerritoryUniversity of MolisePescheItaly
  5. 5.Department of Information EngineeringUniversity of PisaPisaItaly

Personalised recommendations