摘要
为深入探究高速公路重型货车交通死亡事故的显著性影响因素,以我国某省969起高速公路重型货车驾驶人负有全部或主要事故责任为研究对象,从人、车、路、环境等方面选取15组影响因素为自变量,以交通事故严重程度为因变量,构建二元logistic模型进行回归分析。结果表明:超速、单车事故(撞固定物、坠车、侧翻等)、危险线形(急弯、陡坡、弯坡组合等)会显著增加死亡事故发生风险,是导致死亡事故发生的重要因素;疲劳驾驶和驾龄在7~11年的驾驶人发生死亡事故概率相对较低,但疲劳驾驶导致的非死亡事故比例较高,仍需重点关注。研究结果可为高速公路交通管理部门制定重型货车交通事故预防对策提供参考。
In order to conduct an in-depth analysis of the significant factors affecting fatal traffic accidents involving heavy trucks on highways,a study was carried out with 969 accidents on highways in a certain province of China,where the heavy truck drivers were fully or mainly responsible.Fifteen influencing factors were selected from aspects of human,vehicle,road,and environment as independent variables,and the severity of traffic accidents was taken as the dependent variable.A binary logistic model was used for regression analysis.The analysis results show that excessive speeding,single-vehicle accidents(crashing into fixed objects,falling off the road,overturning,etc.),and hazardous alignment(sharp curves,steep slopes,combination of curves and slopes,etc.)can significantly increase the risk of fatal accidents and are important factors leading to fatal accidents.Conversely,fatigue driving and drivers with 7~11 years of driving experience were associated with a lower probability of fatal accidents,though fatigue driving showed a higher incidence of non-fatal accidents,warranting continued attention.The study offers actionable insights for developing heavy-truck accident prevention measures in freeways traffic management.
作者
范贤涛
马平
田亚杰
FAN Xiantao;MA Ping;TIAN Yajie(Department of Public Security Administration,Jiangsu Police Institute,Nanjing 210031,China;Jiangsu Intelligent Connected Traffic Safety Facility Engineering Research Center,Nanjing 210031,China;Science and Technology Division,Traffic Police Department of Shanghai Municipal Public Security Bureau,Shanghai 200120,China;Nanhai Branch of Foshan Public Security Bureau,Foshan 528200,China)
出处
《交通与运输》
2025年第3期93-98,共6页
Traffic & Transportation
基金
2024年度江苏高校哲学社会科学研究一般项目(2024SJYB0338)
公安部2024年度公安软科学类重点项目(2024LL19)。
