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# Statistical Analysis of Accident Proneness of Drivers

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V.V. Dementienko, S.V. Gerus

This work is devoted to the accident rate problem of the driving population. The question under investigation is that to what extent drivers are alike or differing with respect to road accident situations; whether there is any accident proneness, greater for one people and smaller for others. The published statistical data on accident rate examination of over 2.5 million drivers, conducted in North Carolina (USA) for a number of years, were used in this work. On the basis of these data it became possible to develop the mathematical model, according to which all drivers can be divided into four categories, each of which possesses accident proneness of its own, characterized by the probability of a road accident. The variation of this characteristic is rather essential for different categories. The probability of an accident within a year depending on the category varies from 0.03 for "normal" and up to 0.9 for " the worst” drivers. The first category of "normal" drivers is the largest one. With an increase of the number of category, the numerical quantity of drivers of the given category decreases, but the probability of an accident sharply increases. Therefore, the greatest contribution to the quantity of road accidents is made by the second category of drivers. The possibility of classifying a driver by categories depending on a number of road accidents caused by him within a certain period of time has been shown. A comparison with probabilistic characteristics received on the basis of the Russian statistical data has been made. It is shown that for Russian professional motor truck drivers these parameters correspond with a good share of accuracy to the parameters of the first category drivers. The aforesaid denotes a good correlation between different statistical data, and also that statistical data from different regions agree with each other quite well and correction of parameters of the model for different territories should not be significant.
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