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What can we learn from telematics car driving data: a survey. (English) Zbl 07525957

Summary: We give a survey on the field of telematics car driving data research in actuarial science. We describe and discuss telematics car driving data, we illustrate the difficulties of telematics data cleaning, and we highlight the transparency issue of telematics car driving data resulting in associated privacy concerns. Transparency of telematics data is demonstrated by aiming at correctly allocating different car driving trips to the right drivers. This is achieved rather successfully by a convolutional neural network that manages to discriminate different car drivers by their driving styles. In a last step, we describe two approaches of using telematics data for improving claims frequency prediction, one is based on telematics heatmaps and the other one on time series of individual trips, respectively.

MSC:

91G05 Actuarial mathematics
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[1] Ayuso, M.; Guillén, M.; Nielsen, J. P., Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data, Transportation, 46, 735-752 (2019)
[2] Ayuso, M.; Guillén, M.; Pérez-Marín, A. M., Telematics and gender discrimination: some usage-based evidence on whether men’s risk of accidents differs from women’s, Risks, 4, 2, Article 10 pp. (2016)
[3] Ayuso, M.; Guillén, M.; Pérez-Marín, A. M., Using GPS data to analyse the distance traveled to the first accident at fault in pay-as-you-drive insurance, Transportation Research. Part C, Emerging Technologies, 68, 160-167 (2016)
[4] Bayat, S.; Babulal, G. M.; Schindler, S. E.; Fagan, A. M.; Morris, J. C.; Mihailidis, A.; Roe, C. M., GPS driving: a digital biomarker for preclinical Alzheimer disease, Alzheimer’s Research & Therapy, 13, Article 115 pp. (2021)
[5] Boucher, J.-P.; Côté, S.; Guillén, M., Exposure as duration and distance in telematics motor insurance using generalized additive models, Risks, 5, 4, Article 54 pp. (2017)
[6] Boucher, J.-P.; Pérez-Marín, A. M.; Santolino, M., Pay-as-you-drive insurance: the effect of the kilometers on the risk of accident, Anales del Instituto de Actuarios Espanoles, 19, 135-154 (2013)
[7] Denuit, M.; Guillén, M.; Trufin, J., Multivariate credibility modelling for usage-based motor insurance pricing with behavioural data, Annals of Actuarial Science, 13, 2, 378-399 (2019)
[8] Duval, F.; Boucher, J.-P.; Pigeon, M., How much telematics information do insurers need for claim classification (2021)
[9] Eling, M.; Kraft, M., The impact of telematics on the insurability of risks, The Journal of Risk Finance, 21, 2, 77-109 (2020)
[10] Esteves-Booth, A.; Muneer, T.; Kirby, H.; Kubie, J.; Hunter, J., The measurement of vehicular driving cycle within the city of Edinburgh, Transportation Research. Part D, Transport and Environment, 6, 3, 209-220 (2001)
[11] Gao, G.; Meng, S.; Wüthrich, M. V., Claims frequency modeling using telematics car driving data, Scandinavian Actuarial Journal, 2019, 2, 143-162 (2019) · Zbl 1411.91280
[12] Gao, G.; Wang, H.; Wüthrich, M. V., Boosting Poisson regression models with telematics car driving data, Machine Learning, 111, 1, 243-272 (2022)
[13] Gao, G.; Wüthrich, M. V., Feature extraction from telematics car driving heatmaps, European Actuarial Journal, 8, 2, 383-406 (2018) · Zbl 1422.91348
[14] Gao, G.; Wüthrich, M. V., Convolutional neural network classification of telematics car driving data, Risks, 7, 1, Article 6 pp. (2019)
[15] Gao, G.; Wüthrich, M. V.; Yang, H., Evaluation of driving risk at different speeds, Insurance. Mathematics & Economics, 88, 108-119 (2019) · Zbl 1425.91222
[16] Geyer, A.; Kremslehner, D.; Muermann, A., Asymmetric information in automobile insurance: evidence from driving behavior, The Journal of Risk and Insurance, 87, 4, 969-995 (2020)
[17] Gneiting, T., Making and evaluating point forecasts, Journal of the American Statistical Association, 106, 494, 746-762 (2011) · Zbl 1232.62028
[18] Gneiting, T.; Raftery, A. E., Strictly proper scoring rules, prediction, and estimation, Journal of the American Statistical Association, 102, 477, 359-378 (2007) · Zbl 1284.62093
[19] Guillén, M.; Nielsen, J. P.; Pérez-Marín, A. M.; Elpidorou, V., Can automobile insurance telematics predict the risk of near-miss events?, North American Actuarial Journal, 24, 1, 22-34 (2020) · Zbl 1437.91392
[20] Guillén, M.; Nielsen, J. P.; Pérez-Marín, A. M., Near-miss telematics in motor insurance, Journal of Risk and Insurance, 88, 3, 569-589 (2021)
[21] Ho, S.-H.; Wong, Y.-D.; Chang, V. W.-C., Developing Singapore driving cycle for passenger cars to estimate fuel consumption and vehicular emissions, Atmospheric Environment, 97, 353-362 (2014)
[22] Hu, X.; Zhu, X.; Ma, Y. L.; Chiu, Y. C.; Tang, Q., Advancing usage-based insurance – a contextual driving risk modelling and analysis approach, IET Intelligent Transport Systems, 13, 3, 453-460 (2019)
[23] Huang, Y.; Meng, S., Automobile insurance classification ratemaking based on telematics driving data, Decision Support Systems, 127, Article 113156 pp. (2019)
[24] Hung, W. T.; Tong, H. Y.; Lee, C. P.; Ha, K.; Pao, L. Y., Development of practical driving cycle construction methodology: a case study in Hong Kong, Transportation Research. Part D, Transport and Environment, 12, 2, 115-128 (2007)
[25] Joubert, J. W.; De Beer, D.; De Koker, N., Combining accelerometer data and contextual variables to evaluate the risk of driver behaviour, Transportation Research. Part F, Traffic Psychology and Behaviour, 41, 80-96 (2016)
[26] Kamble, S. H.; Mathew, T. V.; Sharma, G. K., Development of real-world driving cycle: case study of Pune, India, Transportation Research. Part D, Transport and Environment, 14, 2, 132-140 (2009)
[27] Klugman, S. A.; Panjer, H. H.; Willmot, G. E., Loss Models: From Data to Decisions (2012), John Wiley & Sons · Zbl 1272.62002
[28] Krüger, F.; Ziegel, J. F., Generic conditions for forecast dominance, Journal of Business & Economics Statistics, 39, 4, 972-983 (2021)
[29] Lemaire, J.; Park, S. C.; Wang, K., The use of annual mileage as a rating variable, ASTIN Bulletin, 46, 1, 39-69 (2016) · Zbl 1390.62213
[30] Ma, Y. L.; Zhu, X.; Hu, X.; Chiu, Y. C., The use of context-sensitive insurance telematics data in auto insurance rate making, Transportation Research. Part A, Policy and Practice, 113, 243-258 (2018)
[31] Meng, S.; Wang, H.; Shi, Y.; Gao, G., Improving automobile insurance claims frequency prediction with telematics car driving data, ASTIN Bulletin: The Journal of the IAA, 1-29 (2022)
[32] Paefgen, J.; Staake, T.; Fleisch, E., Multivariate exposure modeling of accident risk: insights from pay-as-you-drive insurance data, Transportation Research. Part A, Policy and Practice, 61, 27-40 (2014)
[33] Richman, R.; Wüthrich, M. V., LocalGLMnet: interpretable deep learning for tabular data (2021)
[34] Selvaraju, R. R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; Batra, D., Grad-cam: visual explanations from deep networks via gradient-based localization, (Proceedings of the IEEE International Conference on Computer Vision (2017)), 618-626
[35] So, B.; Boucher, J.-P.; Valdez, E. A., Synthetic dataset generation of driver telematics, Risks, 9, 4, Article 58 pp. (2021)
[36] So, B.; Boucher, J.-P.; Valdez, E. A., Cost-sensitive multi-class AdaBoost for understanding behavior based on telematics, ASTIN Bulletin, 51, 719-751 (2021) · Zbl 1480.91243
[37] Sun, S.; Bi, J.; Guillén, M.; Pérez-Marín, A. M., Assessing driving risk using internet of vehicles data: an analysis based on generalized linear models, Sensors, 20, 9, Article 2712 pp. (2020)
[38] Verbelen, R.; Antonio, K.; Claeskens, G., Unraveling the predictive power of telematics data in car insurance pricing, Journal of the Royal Statistical Society. Series C. Applied Statistics, 67, 1275-1304 (2018)
[39] Wahlström, J.; Skog, I.; Händel, P., Detection of dangerous cornering in GNSS-data-driven insurance telematics, IEEE Transactions on Intelligent Transportation Systems, 16, 6, 3073-3083 (2015)
[40] Wang, Q.; Huo, H.; He, K.; Yao, Z.; Zhang, Q., Characterization of vehicle driving patterns and development of driving cycles in Chinese cities, Transportation Research. Part D, Transport and Environment, 13, 5, 289-297 (2008)
[41] Weidner, W.; Transchel, F. W.G.; Weidner, R., Classification of scale-sensitive telematic observables for riskindividual pricing, European Actuarial Journal, 6, 1, 3-24 (2016) · Zbl 1415.91167
[42] Weidner, W.; Transchel, F. W.G.; Weidner, R., Telematic driving profile classification in car insurance pricing, Annals of Actuarial Science, 11, 2, 213-236 (2017)
[43] Wiatowski, T.; Bölcskei, H., A mathematical theory of deep convolutional neural networks for feature extraction, IEEE Transactions on Information Theory, 64, 3, 1845-1866 (2018) · Zbl 1390.94053
[44] Wüthrich, M. V., Covariate selection from telematics car driving data, European Actuarial Journal, 7, 1, 89-108 (2017) · Zbl 1394.62151
[45] Wüthrich, M. V.; Merz, M., Editorial: yes, we CANN!, ASTIN Bulletin, 49, 1, 1-3 (2019)
[46] Wüthrich, M. V.; Merz, M., Statistical foundations of actuarial learning and its applications, SSRN, Article 3822407 pp. (2021)
[47] Zhu, R.; Wüthrich, M. V., Clustering driving styles via image processing, Annals of Actuarial Science, 15, 2, 276-290 (2021)
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