How to Cite?
Chantieva Milana, Gematudinov Rinat, Dzhabrailov Khizar, Gorodnichev Mikhail, "Determination of the psychotypes of drivers using machine learning algorithms using twostage cluster analysis," International Journal of Computer Trends and Technology, vol. 68, no. 9, pp. 1-10, 2020. Crossref, 10.14445/22312803/IJCTT-V68I9P101
This article examines the literature on mathematical methods, and the theory of traffic flows in the 1930s. These problems, both complex sociotechnical systems, particularly, and in general, still attract the attention of researchers studying traffic in megacities. A number of researchers, based on practical data, prove that the results of the hydrodynamic approach to solving traffic problems were more modest than in the original environment, that is, in a liquid. This has previously been noticed by researchers who have noticed the limited usefulness of the entered relationships. Despite numerous attempts to reduce the model to mathematical physics equations, their authors rarely examined the proposed approaches for applicability. Often there are no strict definitions and sufficient conditions for their operation. It is noted that the problem of micro-vibrations in liquids - and especially cavitation - is rather complicated and remains open for further research. As a socio-technical system, generally speaking, the flow of cars has no relation of speed to density in a wide range of partially connected states: an increase in the number of vehicles per unit of carriageway does not have much effect on speed.
The work considered the problem of recognizing the psychotypes of drivers - a very urgent task today. The solution to such a problem as car accidents and fatal accidents is the interest of many, if not everyone. There are already braking systems that can detect objects around the car, road tracking systems that follow the markings using video cameras.
RFBR, project number 19-29-06036, funded the reported study.
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Machine learning model, deep learning, neural network, probabilistic modeling, input processing, data analysis, clustering algorithms, computer software, speed mode, cluster, and recognition of drivers` psychotypes.