Driver Clustering According to the Ratio of Dangerous Behavior Using Machine Learning Algorithms

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Abstract

The paper conducts the research of defining dangerous driving of a vehicle using signals collected during the ride. A number of modern clustering models for drivers segmentation on classes based on the ratio of dangerous driving was used. New approach of data aggregation aiming to cluster data by signal distribution histograms was developed. Achieved results could be used in commercial systems that monitor the quality of drivers behavior in retrospective.

General Information

Keywords: clustering, distribution histograms, dangerous driving, monitoring systems, machine learning

Journal rubric: Data Analysis

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2022120101

Received: 04.03.2022

Accepted:

For citation: Badanina N.D., Sudakov V.A. Driver Clustering According to the Ratio of Dangerous Behavior Using Machine Learning Algorithms. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2022. Vol. 12, no. 1, pp. 5–15. DOI: 10.17759/mda.2022120101. (In Russ., аbstr. in Engl.)

References

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Information About the Authors

Natalya D. Badanina, Master Student, Moscow Aviation Institute (National Research University), Programmer, Keldysh Institute of Applied Mathematics (Russian Academy of Sciences), Moscow, Russia, ORCID: https://orcid.org/0000-0002-5301-1526, e-mail: natashabadanina99@gmail.com

Vladimir A. Sudakov, Doctor of Engineering, Professor of Department 805, Moscow Aviation Institute (MAI), Leading Researcher, Keldysh Institute of Applied Mathematics (Russian Academy of Sciences), Moscow, Russia, ORCID: https://orcid.org/0000-0002-1658-1941, e-mail: sudakov@ws-dss.com

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