基于改进TOPSIS的道路交通风险网络排序研究

doi:10.3969/j.issn.1674-0696.2022.04.06

重庆交通大学学报（自然科学版） ›› 2022, Vol. 41 ›› Issue (04): 33-39.DOI: 10.3969/j.issn.1674-0696.2022.04.06

• 交通+大数据人工智能 • 上一篇下一篇

基于改进TOPSIS的道路交通风险网络排序研究

戴剑勇1,2，黄晓庆1，王雯雯1

(1. 南华大学资源环境与安全工程学院，湖南衡阳 421001； 2. 南华大学核设施应急安全作业技术与装备湖南省重点实验室，湖南衡阳 421001)

收稿日期:2020-11-18 修回日期:2021-03-30 发布日期:2022-04-13
作者简介:戴剑勇（1969—），男，湖南新化人，教授,博士，主要从事安全系统工程与风险管理等方面的研究。E-mail:daijy13@163.com
基金资助:
湖南省教育厅重点资助科研项目 (18A235)

Ranking of Road Traffic Risk Network Based on Improved TOPSIS

DAI Jianyong1,2, HUANG Xiaoqing1, WANG Wenwen1

(1. School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, Hunan, China; 2. Hunan Province Key Laboratory of Emergency Safety Technology and Equipment for Nuclear Facilities, University of South China, Hengyang 421001, Hunan, China)

Received:2020-11-18 Revised:2021-03-30 Published:2022-04-13

摘要/Abstract

摘要： 研究高速公路交通事故风险重要度并进行排序，对提高风险控制效率，减少交通事故的发生的重复性具有重要意义。为获得风险全面且客观的排序，首先应用人因分析及分类模型确定道路交通安全风险及分类，构建道路交通风险网络模型；然后以网络中心性为指标，建立改进的TOPSIS方法（PPR-TOPSIS）对网络中的风险综合排序。运用此法分析京港澳高速公路衡阳段“6·29”重大道路交通事故，并与熵权TOPSIS法对比，结果表明：疲劳驾驶对事故影响最大，贴近度超过80%，企业及运管局的监督管理因素对事故影响较大，贴近度超过50%，PPR-TOPSIS比熵权TOPSIS法更具有综合性与准确度，更适用于重大交通事故。

关键词: 交通运输工程 ;道路交通；安全风险；复杂网络；人因分析及分类；PPR- TOPSIS方法

Abstract: Studying and ranking the risk importance of highway traffic accidents is of great significance for improving the efficiency of risk control and reducing the repetition of traffic accidents. In order to obtain a comprehensive and objective ranking of risks, firstly, the human factors analysis and classification system (HFACS) model was applied to determine the road traffic safety risk and classification, and the road traffic risk network model was constructed. Then, taking network centrality as the index, an improved TOPSIS method (PPR-TOPSIS) was established to comprehensively rank the risks in the network. The proposed method was used to analyze the “6.29” major road traffic accident in Hengyang section of Beijing-Hong Kong-Macao Expressway and compared with entropy weight TOPSIS method. The results show that the influence of fatigue driving on the accident is maximum, whose closeness is more than 80%; and the influence of supervision and management factors of both enterprises and TBA is relatively larger, whose closeness is more than 50%. And PPR-TOPSIS method is more comprehensive and accurate, which is more suitable for major traffic accidents.

Key words: traffic and transportation engineering; road traffic; safety risk; complex networks; HFACS model; PPR-TOPSIS method

中图分类号:

U491.3

戴剑勇1,2，黄晓庆1，王雯雯1. 基于改进TOPSIS的道路交通风险网络排序研究[J]. 重庆交通大学学报（自然科学版）, 2022, 41(04): 33-39.

DAI Jianyong1,2, HUANG Xiaoqing1, WANG Wenwen1. Ranking of Road Traffic Risk Network Based on Improved TOPSIS[J]. Journal of Chongqing Jiaotong University(Natural Science), 2022, 41(04): 33-39.

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基于改进TOPSIS的道路交通风险网络排序研究

Ranking of Road Traffic Risk Network Based on Improved TOPSIS

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