School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran. and Road Safety Research Center, Iran University of Science & Technology, Tehran, Iran. Email:
School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran. and Road Safety Research Center, Iran University of Science & Technology, Tehran, Iran.
J Inj Violence Res. 2022 Jan;14(1):75-88. doi: 10.5249/jivr.v14i1.1679. Epub 2022 Feb 6.
Fatigue and drowsiness accidents are more likely to cause serious injuries and fatalities than other accidents. Statistics revealed that 20 to 40 percent of traffic accidents in Iran are due to drivers' fatigue. This study identified the most important factors affecting driver injuries in fatigue and drowsiness accidents.
The Classification and Regression Tree method (CART) was applied 11,392 drivers were in-volved in fatigue and drowsiness accidents in three provinces of Iran, over the 7 years from 2011-2018. A two-level target variable was used to increase the accuracy of the model. First, dataset in each of three provinces was classified into homogeneous clusters using a two-step clus-tering algorithm. Oversampling method was used for imbalanced accident severity datasets. Then, classification was improved by boosting method.
The classification tree reveals that the month, time of day, collision type, and vehicle type were common factors. Also, driver's age was important in female drivers cluster; the geometry of the place and seat belt/helmet usage were important in urban roads cluster; and area type, road type, road direction, and vehicle factor were important in rural roads cluster. Also, the combination of the CART algorithm with oversampling and boosting increased the accuracy of the models.
The analysis results revealed motorcycles, lack of using a helmet or seat belt, curvy roads, roads with two-way undivided and one-way movement direction increased the injury and death of drivers. Collision with fixed object, run-off-road, overturning, falling, and defective vehicles increased the severity of accidents. Female drivers older than 44 years old have a higher probability of fatality. Identifying the factors affecting the severity of driver injuries in such accidents in each province could assist in determining engineering countermeasures and training educational programs to mitigate these crash severities.
疲劳和困倦引发的事故比其他事故更有可能导致严重伤害和致命后果。统计数据显示，伊朗 20%至 40%的交通事故是由驾驶员疲劳引起的。本研究确定了影响疲劳和困倦事故中驾驶员受伤的最重要因素。
采用分类回归树方法（CART），对 2011 年至 2018 年 7 年间伊朗三个省份的 11392 名疲劳和困倦事故驾驶员进行了分析。使用两级目标变量来提高模型的准确性。首先，使用两步聚类算法对每个省份的数据集进行分类，将其分为同质聚类。对于不平衡的事故严重程度数据集，使用过采样方法。然后，使用提升方法来改进分类。
分类树显示，月份、一天中的时间、碰撞类型和车辆类型是常见因素。此外，驾驶员年龄在女性驾驶员聚类中很重要；地点的几何形状和安全带/头盔的使用在城市道路聚类中很重要；而在农村道路聚类中，区域类型、道路类型、道路方向和车辆因素很重要。此外，CART 算法与过采样和提升相结合，提高了模型的准确性。
分析结果表明，摩托车、不使用头盔或安全带、弯道、双向无分隔和单向行驶方向的道路增加了驾驶员的受伤和死亡风险。与固定物体碰撞、驶离道路、翻车、坠落和有缺陷的车辆增加了事故的严重程度。44 岁以上的女性驾驶员死亡的可能性更高。确定每个省份此类事故中影响驾驶员受伤严重程度的因素，可以有助于确定工程对策和培训教育计划，以减轻这些碰撞的严重程度。
