Citation

Xi J, Zhao Y, Li Z, Jiang Y, Feng W, Ding T. Int. J. Environ. Res. Public Health 2022; 19(23): e15959.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/ijerph192315959

PMID

36498032

Abstract

Taking truck drivers' braking patterns as the research objects, this study used a large amount of truck running data. A recognition method of truck drivers' braking patterns was proposed to determine the distribution of braking patterns during the operation of trucks. First, the segmented data of braking behaviors were collected in order to extract 25 characteristic parameters. Additionally, seven main correlation factors were obtained by dimensionality reduction. The FCM clustering algorithm and CH scores were used to identify nine categories of truck drivers' braking behaviors. Then the LDA2vec model was used to identify the distribution of different braking behavior words in braking patterns, and three categories of truck drivers' braking patterns were identified. The test results showed that the accuracy of the truck drivers' braking pattern recognition model based on LDA2vec was higher than 85%, and braking patterns of drivers in the daily operation process could be mined from vehicle operation data. Furthermore, through the monitoring and pre-warning of the braking patterns and targeted training of drivers, traffic accidents could be avoided. At the same time, this paper's results can be used to protect human life and health and reduce environmental pollution caused by traffic congestion or traffic accidents.

Language: en

Keywords

braking behavior; braking pattern; FCM and LDA2vec; truck operation data