Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.
Int J Environ Res Public Health. 2020 Dec 3;17(23):9020. doi: 10.3390/ijerph17239020.
Understanding the association between crash attributes and drivers' crash involvement in different types of crashes can help figure out the causation of crashes. The aim of this study was to examine the involvement in different types of crashes for drivers from different age groups, by using the police-reported crash data from 2014 to 2016 in Shenzhen, China. A synthetic minority oversampling technique (SMOTE) together with edited nearest neighbors (ENN) were used to solve the data imbalance problem caused by the lack of crash records of older drivers. Logistic regression was utilized to estimate the probability of a certain type of crashes, and odds ratios that were calculated based on the logistic regression results were used to quantify the association between crash attributes and drivers' crash involvement in different types of crashes. Results showed that drivers' involvement patterns in different crash types were affected by different factors, and the involvement patterns differed among the examined age groups. Knowledge generated from the present study could help improve the development of countermeasures for driving safety enhancement.
理解事故属性与不同类型事故中驾驶员事故参与之间的关系有助于找出事故的原因。本研究旨在通过使用 2014 年至 2016 年中国深圳警方报告的事故数据，研究不同年龄组驾驶员在不同类型事故中的参与情况。采用合成少数过采样技术（SMOTE）和编辑最近邻法（ENN）来解决由于缺乏老年驾驶员的事故记录而导致的数据不平衡问题。使用逻辑回归来估计特定类型事故的概率，根据逻辑回归结果计算的比值比用于量化事故属性与不同类型事故中驾驶员事故参与之间的关系。结果表明，驾驶员在不同类型事故中的参与模式受不同因素的影响，并且在研究的年龄组之间存在差异。本研究产生的知识有助于改善提高驾驶安全对策的制定。
