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2车碰撞事故下5座乘用车乘员死亡风险分析 被引量:2

An Analysis of Occupant Death Risk of 5-Seater Cars in Two-vehicle Collisions
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摘要 为探究5座乘用车乘员在2车碰撞事故下的死亡风险,研究了6种单一特征变量对乘员致死率的影响,进而基于二项Logistic回归模型分别对单一特征变量和组合特征变量进行显著性分析。通过9种常用的分类算法,结合网格搜索的调参方法,以F1为衡量指标选出相对较优的3种分类算法,即投票分类器、梯度提升及决策树,来构建多特征组合下的死亡风险预测模型。研究结果表明:①单一特征变量中行驶方向、路段类型、碰撞对象、乘坐位置对乘员死亡有显著影响。其中,异向行驶的车辆碰撞与同向行驶相比,乘员的死亡风险增加72%;非高速交叉路段与高速路段相比,乘员的死亡风险降低69%;碰撞对象为商用货车、商用客车的乘员死亡风险分别是乘用车的5倍和3倍,若在非高速非交叉路段发生碰撞则乘员死亡风险升至8倍左右,若在高速路段则高达15倍左右;相对于驾驶位乘员,副驾驶位乘员的死亡风险增加70%,且该位置乘员的死亡风险在高速路段会升高到驾驶位乘员的近4倍;②碰撞对象和路段类型是影响乘员死亡情况的主要特征变量;③由模型的预测结果可知:5座乘用车的正面或后面与商用货车在高速路段或非高速非交叉路段发生碰撞,乘员的死亡风险高于生存几率。 To study occupant death risk of 5-seater cars in two-vehicle collisions, the impacts of six variables on fatality rates are compared. Moreover, binary logistic regression is used to analyze the influences of different features and their combinations on occupant death risk. A model to forecast occupant death risk is developed based on a parameter adjustment method of GridSearchCV, three algorithms with larger F1 values are selected from nine classification algorithms, which are voting classifier, gradient boosting, and decision tree. The results show that:(1) travel direction, type of road sections, type of crushed vehicles, and different seats have significant effects on occupant death risk. The occupant death risk increases by 72% when compared accidents including vehicles driving in opposite directions with that including vehicles driving in the same direction. The risk decreases by 69% when compared accidents occur at non-freeway intersections with that occur at freeway sections. The occupant death risks for commercial trucks and commercial buses are 5 times and 3 times higher than passenger cars, respectively. The risk rises to around 8 times and 15 times for non-freeway non-intersection sections and freeway sections, respectively. The death risk for passenger seats increased by 70% compared with that for driver seat, and the death risk for passenger seat is nearly 4 times higher than that for driver seat at freeway sections.(2) Different vehicles and road-section types are the most important features affecting occupant death.(3) The proposed model indicates that if a commercial truck collides at the front or rear of a 5-seater car at freeway sections or non-freeway non-intersection sections,the occupants have higher risk of death than chance of survival.
作者 占隽均 云美萍 张韡 董怡佳 ZHAN Junjun;YUN Meiping;ZHANG Wei;DONG Yijia(The Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Tongji University,Shanghai 201804,China;School of Automobile,Chang'an University,Xi'an 710064,China)
出处 《交通信息与安全》 CSCD 北大核心 2022年第4期46-53,共8页 Journal of Transport Information and Safety
基金 国家自然科学基金项目(52072264)资助。
关键词 交通安全 5座乘用车 风险分析 机器学习 特征组合 traffic safety 5-seater cars risk analysis machine learning feature crosses
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