中国安全科学学报 ›› 2020, Vol. 30 ›› Issue (5): 143-148.doi: 10.16265/j.cnki.issn1003-3033.2020.05.022

• 公共安全 • 上一篇    下一篇

高速道路追尾感知PC-Crash仿真与统计建模

黄西子1,2, 黄淑萍1,2 副教授   

  1. 1.上海交通大学 船舶海洋与建筑工程学院,上海 200240;
    2.上海交通大学 海洋工程国家重点实验室,上海 200240
  • 收稿日期:2020-02-08 修回日期:2020-04-08 出版日期:2020-05-28 发布日期:2021-01-28
  • 作者简介:黄西子(1996—),女,浙江温州人,硕士研究生,研究方向为道路交通安全。E-mail:hxz630@sjtu.edu.cn。

Freeway rear-end perception model based on PC-Crash simulation and statistical analysis

HUANG Xizi1,2, HUANG Shuping1,2   

  1. 1. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China;
    2. State Key Laboratory of Ocean Engineering, Shanghai Jiaotong University, Shanghai 200240, China
  • Received:2020-02-08 Revised:2020-04-08 Online:2020-05-28 Published:2021-01-28

摘要: 为防范和减少高速道路追尾事故,提出一种基于PC-Crash仿真与统计分析的高速道路追尾事故感知模型。以事故比例较大的小型车之间的基础碰撞模型为研究对象,首先,基于PC-Crash第一类参数分析单因素和正交试验的参数敏感性,确定以冲入速度为指标的追尾避撞主要参数,前3名影响参数反应时间、初速度和减速度;其次,运用统计分析软件SPSS和Stata分别建立基于仿真得出的270组冲入速度的回归模型;然后通过典型高速公路事故实证,确定最佳感知测度模型。经检验分析,二分类Logistics回归模型能较为准确地判断出当前跟车距离是否安全。

关键词: 高速道路, 追尾感知模型, PC-Crash仿真, 多元线性回归, 逐步Bayes判别, 二分类Logistics回归模型

Abstract: In order to prevent and reduce freeway rear-end collisions, a perception model based on PC-Crash simulation and statistical analysis is proposed. With research object of basic collision model between small cars which made a high proportion of rear-end collision, parameter sensitivity of single factor and orthogonal test was analyzed based on first type of parameters in PC-Crash so as to determine main parameters of rear-end avoidance with rushing speed as index, and the top three factors that have a greater impact were reaction time, initial velocity, and deceleration. Then, a regression model based on PC-Crash simulation of 270 sets of rushing speed was established by using statistical analysis software SPSS and Stata. Finally, an optimal perception measurement model was determined with empirical evidence of typical freeway accidents. The analysis results show that the two-class Logistics regression model could determine whether following distance is safe more accurately.

Key words: freeway, rear-end perception model, PC-Crash simulation, multiple linear regression, Bayes stepwise discriminant model, two-class Logistics regression

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