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Convolutional Neural Network Classification of Telematics Car Driving Data

Author

Listed:
  • Guangyuan Gao

    (Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing 100872, China)

  • Mario V. Wüthrich

    (RiskLab, Department of Mathematics, ETH Zurich, 8092 Zürich, Switzerland)

Abstract

The aim of this project is to analyze high-frequency GPS location data (second per second) of individual car drivers (and trips). We extract feature information about speeds, acceleration, deceleration, and changes of direction from this high-frequency GPS location data. Time series of this feature information allow us to appropriately allocate individual car driving trips to selected drivers using convolutional neural networks.

Suggested Citation

  • Guangyuan Gao & Mario V. Wüthrich, 2019. "Convolutional Neural Network Classification of Telematics Car Driving Data," Risks, MDPI, vol. 7(1), pages 1-18, January.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:1:p:6-:d:196466
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    References listed on IDEAS

    as
    1. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2018. "Unravelling the predictive power of telematics data in car insurance pricing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1275-1304, November.
    2. Weidner, Wiltrud & Transchel, Fabian W.G. & Weidner, Robert, 2017. "Telematic driving profile classification in car insurance pricing," Annals of Actuarial Science, Cambridge University Press, vol. 11(2), pages 213-236, September.
    3. 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.
    4. Lemaire, Jean & Park, Sojung Carol & Wang, Kili C., 2016. "The Use Of Annual Mileage As A Rating Variable," ASTIN Bulletin, Cambridge University Press, vol. 46(1), pages 39-69, January.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Christopher Blier-Wong & Hélène Cossette & Luc Lamontagne & Etienne Marceau, 2020. "Machine Learning in P&C Insurance: A Review for Pricing and Reserving," Risks, MDPI, vol. 9(1), pages 1-26, December.
    2. Montserrat Guillen & Jens Perch Nielsen & Ana M. Pérez‐Marín, 2021. "Near‐miss telematics in motor insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 569-589, September.
    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. 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.
    5. Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2019. "Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression," Risks, MDPI, vol. 7(2), pages 1-16, June.
    6. 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.

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