人车碰撞事故中行人伤亡风险的关联性分析与预测

Correlation Analysis and Prediction of Pedestrian Casualty Risk in Car-Pedestrian Collision Accident

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%.