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

被引：0
|
作者：
Lan F. [1 ]
Zhang Y. [1 ]
Chen J. [1 ]
Feng Y. [1 ]
Zhou Y. [1 ]
机构：
[1] School of Mechanical and Automotive Engineering/Guangdong Provincial Key Laboratory of Automotive, South China University of Technology, Guangzhou
来源：
Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2022年 / 50卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Accident characteristics; BP neural network; Car-pedestrian collision accident; Casualty prediction; Cluster analysis; Pedestrian casualty;
D O I：
10.12141/j.issn.1000-565X.210058
中图分类号：
学科分类号：
摘要：
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%. © 2022, Editorial Department, Journal of South China University of Technology. All right reserved.
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页码：1 / 10
页数：9
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