Journal of South China University of Technology(Natural Science Edition) ›› 2022, Vol. 50 ›› Issue (5): 1-10.doi: 10.12141/j.issn.1000-565X.210058

Special Issue: 2022年交通运输工程

• Traffic & Transportation Engineering • Previous Articles     Next Articles

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

LAN Fengchong ZHANG Yue CHEN Jiqing FENG Yujia ZHOU Yunjiao   

  1. School of Mechanical and Automotive Engineering/Guangdong Provincial Key Laboratory of Automotive，South China University of Technology，Guangzhou 510640，China
  • Received:2021-02-02 Revised:2022-01-16 Online:2022-05-25 Published:2022-02-18
  • Contact: 陈吉清（1966-），女，博士，教授，主要从事汽车结构设计优化与安全技术研究。 E-mail:chjq@scut.edu.cn
  • About author:兰凤崇（1959-），男，博士，教授，主要从事汽车结构设计优化与安全技术研究。E-mail：fclan@scut.edu.cn
  • Supported by:

    Supported by the National Natural Science Foundation of China（52175267）

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Abstract:

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%.

Key words: car-pedestrian collision accident, pedestrian casualty, accident characteristics, cluster analysis, casualty prediction, BP neural network

CLC Number: