摘要
为了研究人车碰撞事故中影响行人伤亡的因素,提出了基于聚类方法与BP神经网络的行人碰撞后伤亡风险预测模型。首先,以国家车辆事故深度调查体系(NAIS)数据库内2018—2019年间的372起人车碰撞事故数据为研究对象,对其进行统计分析,得到关于车辆、行人和碰撞状态3个维度的9项事故特征参数;然后,结合各事故特征特性,对连续特征值选用K均值聚类方法,对离散特征值选用层次聚类法,分析行人的伤亡风险与各特征参数间的关联性;最后,建立BP神经网络预测模型,根据事故特征预测行人伤亡情况。结果表明建立的行人伤亡风险预测模型的成功率为86%。
A model for predicting the risks of pedestrian injuries after accidents based on clustering method and back-propagation neural network was proposed to study the factors that affect pedestrian injuries in car-pedestrian collision accidents.Firstly,the data of 372 car-pedestrian collision accidents between 2018 and 2019 in the National Vehicle Accident In-depth Investigation System(NAIS)database was collected as the research object.And it was statistically analyzed to obtain 9 accident characteristic parameters in three dimensions of vehicle,pedestrian and collision status.Then,combined with the characteristics of each accident,the K-means clustering method was selected for continuous eigenvalues,and the hierarchical clustering method was selected for discrete eigenvalues to obtain the correlation between pedestrian injury and death risks and various characteristic parameters.Finally,a BP neural network prediction model based on accident characteristics was established to predict pedestrian injuries and deaths.The test results show that the success rate of the pedestrian casualty risk prediction model is 86%.
作者
兰凤崇
张越
陈吉清
冯雨佳
周云郊
LAN Fengchong;ZHANG Yue;CHEN Jiqing;FENG Yujia;ZHOU Yunjiao(School of Mechanical and Automotive Engineering/Guangdong Provincial Key Laboratory of Automotive,South China University of Technology,Guangzhou 510640,China)
出处
《华南理工大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2022年第5期1-10,共10页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(52175267)
国家车辆事故深度调查体系资助项目(NAIS-ZL-ZHGT-2020014)。
关键词
人车碰撞事故
行人伤亡
事故特征
聚类分析
伤亡预测
BP神经网络
car-pedestrian collision accident
pedestrian casualty
accident characteristics
cluster analysis
casualty prediction
BP neural network