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中国职业重型半挂牵引车驾驶员的驾驶行为与交通事故参与情况。

Driver behaviour and traffic accident involvement among professional heavy semi-trailer truck drivers in China.

机构信息

Shanghai Urban Operation (Group) Co., Ltd, Shanghai, China.

College of Transportation Engineering, Chang'an University, Xi'an, Shaanxi, China.

出版信息

PLoS One. 2021 Dec 2;16(12):e0260217. doi: 10.1371/journal.pone.0260217. eCollection 2021.

DOI:10.1371/journal.pone.0260217
PMID:34855802
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8638885/
Abstract

The purpose of this study was to develop a driving behavior scale for professional drivers of heavy semi-trailer trucks in China, and study the causes of such driving behavior and its impact on traffic safety operation. Data was processed by IBM SPSS 25. In addition to principal component analysis, Promax rotation, Bartlett's test, Cronbach's alpha, correlation analysis and binary logistic regression were examined. A DBQ with 4 dimensions and 20 items, and a PDBQ with 1 dimension and 6 items were developed for professional drivers of heavy semi-trailer trucks in China. The KMO coefficients of PDBQ and DBQ were 0.822 and 0.852, respectively, and the significant level of Bartlett's popularity test was p < 0.0001. The accident prediction model showed that the variables related to traffic accidents were negligence/lapses and driving time of heavy semi-trailer truck drivers. 1-5 a.m. was found to be the most dangerous period for drivers of medium and heavy semi-trailer trucks, during which accidents were most likely to happen. As negligence/lapses increased by one unit, the probability of traffic accidents increased by 2.293 times.

摘要

本研究旨在开发中国重型半挂牵引车职业驾驶员驾驶行为量表,并研究其驾驶行为的原因及其对交通安全运行的影响。数据采用 IBM SPSS 25 进行处理。除主成分分析外,还进行了 Promax 旋转、Bartlett 检验、Cronbach's alpha、相关分析和二元逻辑回归检验。为中国重型半挂牵引车职业驾驶员开发了具有 4 个维度和 20 个项目的 DBQ,以及具有 1 个维度和 6 个项目的 PDBQ。PDBQ 和 DBQ 的 KMO 系数分别为 0.822 和 0.852,Bartlett 普及检验的显著水平为 p<0.0001。事故预测模型表明,与交通事故相关的变量是疏忽/失误和重型半挂牵引车驾驶员的驾驶时间。1-5 点被认为是中型和重型半挂牵引车驾驶员最危险的时间段,在此期间最有可能发生事故。当疏忽/失误增加一个单位时,发生交通事故的概率增加 2.293 倍。