基于24Model的重特大道路交通事故致因网络分析

Causal Network Analysis of Major Traffic Accidents Avenue Based on 24Model

为探究导致重特大道路交通事故的致因因素及其演化关系,运用24Model分析收集的62起重特大道路交通事故调查报告,从不安全动作因素、不安全物态因素、个体安全能力、管理体系因素、安全文化因素以及外部环境因素6类因素展开分析,确定了53个致因因素,进一步构建重特大道路交通事故致因网络,并分析该网络模型的相关拓扑特征指标,结果表明:安全知识不足,安全意识淡薄,安全习惯不佳,心理状态不佳等10项致因因素中心性较高,处于社会网络核心位置,易于诱发其他致因因素,网络的平均路径长度以及网络直径较小,这表明该致因网络具有小世界性,进一步分析社会网络边权占比,得到了1条致因因素关键影响路径,针对关键致因因素及路径进行协同控制对预防重特大道路交通事故发生具有重要意义。

To explore the causal factors and their evolutionary relationships that lead to traffic accidents on major roads,a survey report of 62 major road accidents was collected using 24Model analysis.Six types of factors were analyzed,including unsafe action factors,unsafe physical conditions,individual safety capabilities,management system factors,safety factors,and external environmental factors.53 causal factors were identified,and a network of causes for traffic accidents on major roads was further constructed,and analyze the relevant topological characteristic indicators of the network model,the results show that:insufficient security knowledge,weak security awareness,poor security habits,poor psychological state,and other 10 causal factors have high centrality,are located at the core of the social network,and are easy to induce other causal factors.The average path length and network diameter of the network are small,indicating that the causal network has small worldliness.Further analysis of the proportion of social network edge weights,obtaining a key influencing path of causative factors is of great significance for the prevention of traffic accidents on major highways through collaborative control of key causative factors and paths.