Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-27T05:53:37.810Z Has data issue: false hasContentIssue false
  • English
  • Français

Clustering driving styles via image processing

Published online by Cambridge University Press:  27 October 2020

Rui Zhu Open the ORCID record for Rui Zhu [Opens in a new window]  and
Mario V. Wüthrich
Rui Zhu*
Affiliation:
Faculty of Actuarial Science and Insurance, The Business School, City, University of London, London EC1Y 8TZ, UK
Mario V. Wüthrich
Affiliation:
RiskLab, Department of Mathematics, ETH Zurich, 8092 Zurich, Switzerland
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

It has become of key interest in the insurance industry to understand and extract information from telematics car driving data. Telematics car driving data of individual car drivers can be summarised in so-called speed–acceleration heatmaps. The aim of this study is to cluster such speed–acceleration heatmaps to different categories by analysing similarities and differences in these heatmaps. Making use of local smoothness properties, we propose to process these heatmaps as RGB images. Clustering can then be achieved by involving supervised information via a transfer learning approach using the pre-trained AlexNet to extract discriminative features. The K-means algorithm is then applied on these extracted discriminative features for clustering. The experiment results in an improvement of heatmap clustering compared to classical approaches.

Keywords

Telematics car driving dataDriving stylesUnsupervised learningImage processingTransfer learningAlexNet
Type
Paper
Information
Annals of Actuarial Science , Volume 15 , Issue 2 , July 2021 , pp. 276 - 290
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Institute and Faculty of Actuaries

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ayuso, M., Guillen, M. & Pérez-Marín, A.M. (2016a). Telematics and gender discrimination: Some usage-based evidence on whether men’s risk of accidents differs from women’s. Risks, 4, 10.CrossRefGoogle Scholar
Ayuso, M., Guillen, M. & Pérez-Marín, A.M. (2016b). 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, 86, 160–167.CrossRefGoogle Scholar
Bian, Y., Yang, C., Zhao, J.L. & Liang, L. (2018). Good drivers pay less: A study of usage-based vehicle insurance models. Transportation Research Part A: Policy and Practice, 107, 20–34.Google Scholar
Boucher, J.P., Côté, S. & Guillen, M. (2017). Exposure as duration and distance in telematics motor insurance using generalized additive models. Risks, 5, 54.CrossRefGoogle Scholar
Carfora, M.F., Martinelli, F., Mercaldo, F., Nardone, V., Orlando, A., Santone, A. & Vaglini, G. (2019). A “pay-how-you-drive” car insurance approach through cluster analysis. Soft Computing, 23, 2863–2875.CrossRefGoogle Scholar
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K. & Li, F.F. (2009). ImageNet: A Large-Scale Hierarchical Image Database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248–255). IEEE.Google Scholar
Denuit, M., Guillen, M. & Trufin, J. (2019). Multivariate credibility modelling for usage-based motor insurance pricing with behavioural data. Annals of Actuarial Science, 13, 378–399.CrossRefGoogle Scholar
European Commission (2012). EU rules on gender-neutral pricing in insurance industry enter into force. Available online at the address https://ec.europa.eu/commission/presscorner/detail/en/IP_12_1430 Google Scholar
Gao, G., Meng, S. & Wüthrich, M.V. (2019). Claims frequency modeling using telematics car driving data. Scandinavian Actuarial Journal, 2019, 143–162.CrossRefGoogle Scholar
Gao, G. & Wüthrich, M.V. (2018). Feature extraction from telematics car driving heatmaps. European Actuarial Journal, 8, 383–406.CrossRefGoogle Scholar
Ho, S.H., Wong, Y.D. & Chang, V.W.C. (2014). Developing singapore driving cycle for passenger cars to estimate fuel consumption and vehicular emissions. Atmospheric Environment, 97, 353–362.CrossRefGoogle Scholar
Hung, W.T., Tong, H., Lee, C., Ha, K. & Pao, L. (2007). Development of a practical driving cycle construction methodology: A case study in hong kong. Transportation Research Part D: Transport and Environment, 12, 115–128.CrossRefGoogle Scholar
James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning. vol. 112. Springer, New York.CrossRefGoogle Scholar
Jolliffe, I.T. (1986). Principal components in regression analysis. In: Principal Component Analysis (pp. 129–155). Springer, New York.CrossRefGoogle Scholar
Kamble, S.H., Mathew, T.V. & Sharma, G.K. (2009). Development of real-world driving cycle: Case study of Pune, India. Transportation Research Part D: Transport and Environment, 14, 132–140.CrossRefGoogle Scholar
Krizhevsky, A., Sutskever, I. & Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (pp. 1097–1105).Google Scholar
Lemaire, J., Park, S.C. & Wang, K.C. (2016). The use of annual mileage as a rating variable. ASTIN Bulletin: The Journal of the IAA, 46, 39–69.CrossRefGoogle Scholar
Pan, S.J. & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345–1359.CrossRefGoogle Scholar
Rousseeuw, P.J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20, 53–65.CrossRefGoogle Scholar
Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D. & Summers, R.M. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35, 1285–1298.CrossRefGoogle ScholarPubMed
Sun, S., Bi, J., Guillen, M. & Pérez-Marín, A.M. (2020). Assessing driving risk using internet of vehicles data: An analysis based on generalized linear models. Sensors 20, 2712.CrossRefGoogle ScholarPubMed
Torrey, L. & Shavlik, J. (2010). Transfer learning. In: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques (pp. 242–264). IGI GlobalCrossRefGoogle Scholar
Verbelen, R., Antonio, K. & Claeskens, G. (2018). Unravelling the predictive power of telematics data in car insurance pricing. Journal of the Royal Statistical Society: Series C (Applied Statistics), 67, 1275–1304.Google Scholar
Weidner, W., Transchel, F.W. & Weidner, R. (2017). Telematic driving profile classification in car insurance pricing. Annals of Actuarial Science 11, 213–236.CrossRefGoogle Scholar
Wüthrich, M.V. (2017). Covariate selection from telematics car driving data. European Actuarial Journal, 7, 89–108.CrossRefGoogle Scholar