背景:美国高速公路上的所有死亡人数中,每年有一半以上是由于道路偏离(RwD)造成的。先前的研究已经探索了导致RwD撞车的各种风险因素,然而,考虑照明条件影响的全面调查没有得到充分解决。
方法:使用路易斯安那州运输和发展部的事故数据库,根据日光和黑暗(有和没有路灯)分析了2008-2017年间农村两车道(R2L)高速公路上发生的致命和伤害RwD撞车事故.
方法:本研究采用一种安全的系统方法来探索多维碰撞风险因素之间有意义的复杂相互作用。要做到这一点,利用无监督数据挖掘算法关联规则挖掘(ARM)。
结论:基于生成的规则,研究结果揭示了白天几种有趣的碰撞模式,黑暗与路灯,和黑暗的无街灯,强调根据照明条件调查RwD碰撞模式的重要性。在日光条件下,致命的RwD事故与多云的天气条件有关,分心的司机,道路上的积水,不使用安全带,和施工区。在黑暗照明条件下(有和没有路灯),大部分RwD事故都与酒精/毒品有关,年轻司机(15-24岁),驾驶员状况(例如,漫不经心,分心,疾病/疲劳/睡着),与动物相撞。
结论:研究结果还揭示了某些驾驶员行为模式与RwD撞车事故的关系,例如酒精/药物中毒与在黑暗无路灯条件下不使用安全带之间的强关联。根据识别的碰撞模式和不同照明条件下的行为特征,这些发现可以帮助研究人员和安全专家制定最有效的RwD碰撞缓解策略。
More than half of all fatalities on U.S. highways occur due to roadway departure (RwD) each year. Previous research has explored various risk factors that contribute to RwD crashes, however, a comprehensive investigation considering the effect of lighting conditions has been insufficiently addressed.
Using the Louisiana Department of Transportation and Development crash database, fatal and injury RwD crashes occurring on rural two-lane (R2L) highways between 2008-2017 were analyzed based on daylight and
dark (with and without streetlight).
This research employed a safe system approach to explore meaningful complex interactions among multidimensional crash risk factors. To accomplish this, an unsupervised data mining algorithm association rules mining (ARM) was utilized.
Based on the generated rules, the findings reveal several interesting crash patterns in the daylight,
dark-with-streetlight, and
dark-no-streetlight, emphasizing the importance of investigating RwD crash patterns depending on the lighting conditions. In daylight condition, fatal RwD crashes are associated with cloudy weather conditions, distracted drivers, standing water on the roadway, no seat belt use, and construction zones. In
dark lighting condition (with and without streetlight), the majority of the RwD crashes are associated with alcohol/drug involvement, young drivers (15-24 years), driver condition (e.g., inattentive, distracted, illness/fatigued/asleep), and colliding with animal(s).
The findings also reveal how certain driver behavior patterns are connected to RwD crashes, such as a strong association between alcohol/drug intoxication and no seat belt usage in the
dark-no-streetlight condition. Based on the identified crash patterns and behavioral characteristics under different lighting conditions, the findings could aid researchers and safety specialists in developing the most effective RwD crash mitigation strategies.