|本期目录/Table of Contents|

[1]胡焱松,王长君,张勇刚,等.货车交通事故严重程度影响因素分析*[J].中国安全生产科学技术,2025,21(4):142-150.[doi:10.11731/j.issn.1673-193x.2025.04.019]
　HU Yansong,WANG Changjun,ZHANG Yonggang,et al.Analysis on factors influencing severity of truck traffic accidents[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2025,21(4):142-150.[doi:10.11731/j.issn.1673-193x.2025.04.019]
点击复制

货车交通事故严重程度影响因素分析*
分享到：

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
21
期数:
2025年4期
页码:
142-150
栏目:
职业安全卫生管理与技术
出版日期:
2025-04-30

文章信息/Info

Title:
Analysis on factors influencing severity of truck traffic accidents
文章编号:
1673-193X(2025)-04-0142-09
作者:
胡焱松; 王长君; 张勇刚; 褚宇航
（1.中国人民公安大学 交通管理学院，北京 100038；
2.广东警官学院 治安与交通管理学院，广东 广州 510230；
3.公安部道路交通安全研究中心，北京 100176)
Author(s):
HU Yansong;  WANG Changjun;  ZHANG Yonggang;  CHU Yuhang
(1.School of Traffic Management,People’s Public Security University of China,Beijing 100038,China;
2.School of Public Security and Traffic Management,Guangdong Police College,Guangzhou Guangdong 510230,China;
3.Research Institute for Road Safety of MPS,Beijing 100176,China)
关键词:
交通安全; 货车; 事故严重程度; 影响因素; 有序Logit回归; 树型贝叶斯网络
Keywords:
traffic safety;  truck;  accident severity;  influencing factor;  ordered Logit regression;  tree-structured Bayesian network
分类号:
X951;U491.3
DOI:
10.11731/j.issn.1673-193x.2025.04.019
文献标志码:
A
摘要:
为探究货车交通事故严重程度的主要影响因素及其耦合关系，基于我国南方某城市2021—2023年货车交通事故数据，采用特征分类、K-means聚类以及特征融合的方法提取初始影响因素，并构建XGBoost模型、有序Logit回归模型和树形贝叶斯网络模型，对影响因素的重要性、单因素和因素耦合进行量化分析。研究结果表明：碰撞对象、道路类型、超载等7个因素对事故严重程度影响显著，其中碰撞对象最为重要；碰撞对象分别与重型车辆、超载、箱式货车、工程运输车辆、转弯变道、有隔离等12个影响因素耦合会大幅提高事故严重程度，当碰撞行人与阴天、超载、工程运输车辆以及转弯变道等耦合时，发生重大事故的概率超过40%。研究结果可为货车交通事故防控提供决策参考。
Abstract:
To investigate the main influencing factors and their coupling relationship in the severity of truck traffic accidents,the initial influencing factors were extracted through the feature classification,K-means clustering,and feature fusion,based on the data of truck traffic accidents from a southern city in China from 2021 to 2023.The XGBoost,ordered Logit regression,and tree-structured Bayesian network models were used to quantitatively analyze the importance of these factors,as well as single-factor and factor coupling effects.The results show that seven factors,including collision object,road type,and overload,significantly influence the accident severity,with the collision object being the most critical.Furthermore,the coupling of the collision object with 12 other factors,such as heavy vehicles,overload,box trucks,engineering transportation vehicles,lane changing or turning,and with barriers,substantially increase the accident severity.When the collision with pedestrians is coupled with overcast weather,overload,engineering transportation vehicles,and lane changing or turning,the probability of major accidents exceeds 40%.The research results can provide decision-making reference for the prevention and control of truck traffic accidents.

参考文献/References:

［1］段雨阳,何雅琴.基于Logistic的不同车型碰撞行人事故严重程度致因分析［J］.中国安全生产科学技术,2022,18(1):175-181. DUAN Yuyang,HE Yaqin,Causes analysis on severity of pedestrian collision accidents with different vehicle types based on Logistic［J］.Journal of Safety Science and Technology,2022,18(1):175-181.
［2］WANG Y G,PRATO C G.Determinantsof injury severity for truck crashes on mountain expressways in China:a case study with a partial proportional odds model ［J］.Safety Science,2019,117:100-107.
［3］YUKSEL A S,ATMACA S.Driver’s black box:a system for driver risk assessment using machine learning and fuzzy Logic［J］.Journal of Intelligent Transportation Systems,2021,25(5):482-500.
［4］乔建刚,范颖蓉,陶瑞,等.山区高速隧道交通事故严重程度预测及特大事故决策规则提取［J］.中国安全生产科学技术,2024,20(7):186-192. QIAO Jiangang,FAN Yingrong,TAO Rui,et al.Prediction of traffic accident severity and extraction of decision rules for extraordinarily serious accident in mountainous high-speed tunnels［J］.Journal of Safety Science and Technology,2024,20(7):186-192.
［5］KATANALP B Y,EREN E.The novel approaches to classify cyclist accident injury-severity:hybrid fuzzy decision mechanisms［J］.Accident Analysis and Prevention,2020,144:105590.
［6］殷豪,林淼,王鹏,等.基于XGBoost的二轮车碾压事故致因研究［J］.安全与环境工程,2023,30(5):19-27,45. YIN Hao,LIN Miao,WANG Peng,et al.Study on the cause of two-wheeler run-over accidents based on XGBoost［J］.Safety and Environmental Engineering,2023,30(5):19-27,45.
［7］SANTOSA K,DIASA J P,AMADO C.A literature review of machine learning algorithms for crash injury severity prediction［J］.Journal of Safety Research,2022,80:254-269.
［8］戢晓峰,吴亚欣,郝京京,等.平纵组合路段事故严重程度致因辨识模型［J］.交通运输系统工程与信息,2020,20(6):197-204. JI Xiaofeng,WU Yaxin,HAO Jingjing,et al.Severity factors analysis model of traffic accident on combined horizontal and vertical alignments［J］.Journal of Transportation Systems Engineering and Information Technology,2020,20(6):197-204.
［9］李硕,马玉坤,韩晖,等.山区高速公路货车事故严重度致因及随机参数分析［J］.公路交通科技,2023,40(4):228-236. LI Shuo,MA Yukun,HAN Hui,et al.Analysis on causes and random parameters of truck accident severity in mountainous expressway［J］.Journal of Highway and Transportation Research and Development,2023,40(4):228-236.
［10］ALI Y,HUSSAIN F,HAQUE M M.Advances,challenges,and future research needs in machine learning-based crash prediction models:a systematic review［J］.Accident Analysis and Prevention,2024,194:107378.
［11］ZHAO L J,WANG X L,QIAN Y.Analysis of factors that influence hazardous material transportation accidents based on Bayesian networks:a case study in China［J］.Safety Science,2012,50:1049-1055.
［12］成卫,马铭炜,张小龙.基于贝叶斯网络的高速公路交通事故严重程度预测及致因分析［J］.重庆交通大学学报(自然科学版),2023,42(7):87-95. CHENG Wei,MA Mingwei,ZHANG Xiaolong.Prediction and cause analysis of freeway traffic accident severity based on Bayesian network［J］.Journal of Chongqing JiaoTong University(Natural Science),2023,42(7):87-95.
［13］ZHU L P,ZHANG Z Q,SONG D D,et al.Analysis of traffic accident causes based on data augmentation and ensemble learning with high-dimensional small-sample data［J］.Expert System with Application,2024,237:121782.
［14］PARSA A B,MOVAHEDI A,TAGHIPOUR H,et al.Toward safer highways,application of XGBoost and SHAP for real-time accident detection and feature analysis［J］.Accident Analysis and Prevention,2020,136:105405.
［15］李泽文,张萌萌,李虹燕.基于最优尺度Logit模型的交通事故形态致因分析［J］.中国安全生产科学技术,2022,18(1):169-174. LI Zewen,ZHANG Mengmeng,LI Hongyan.Cause analysis of traffic accident forms based on optimal scale Logit model［J］.Journal of Safety Science and Technology,2022,18(1):169-174.
［16］陈强.高级计量经济学及 Stata 应用［M］.北京:高等教育出版社,2014.
［17］WANG L M,LI L L,LI Q L，et al.Learning high-dependence Bayesian network classifier with robust topology［J］.Expert Systems with Applications,2024,239:122395.
［18］HEINRICH G.Parameter estimation for text analysis［EB/OL］.(2008-08)［2025-03-26］.https://www2.cs.uh.edu/~arjun/courses/advnlp/text-est_heinrich.pdf.
［19］SUN Z,XING Y,WANG J,et al.Exploring injury severity of vulnerable road user involved crashes across seasons:a hybrid method integrating random parameter logit model and Bayesian network［J］.Safety Science,2022,150:105682-105692.
［20］胡立伟,何越人,李耀平,等.基于人工免疫机制的营运货车运行风险评价研究［J］.交通运输系统工程与信息,2021,21(1):149-155. HU Liwei,HE Yueren,LI Yaoping，et al.Commercial truck driving risk evaluation based on artificial immune mechanism［J］.Journal of Transportation Systems Engineering and Information Technology,2021,21(1):149-155.
［21］任杰,李晓虎,景云超,等.基于违规行为的公交驾驶员事故风险因素研究［J］.武汉理工大学学报(交通科学与工程版),2021,45(4):633-638. REN Jie,LI Xiaohu,JING Yunchao,et al.Research on risk factors of public traffic driver accidents based on violation［J］.Journal of Wuhan University of Technology (Transportation Science & Engineering),2021,45(4):633 -638.

相似文献/References:

[1]陈 春.道路交通事故的影响因素研究——基于结构方程模型的实证研究[J].中国安全生产科学技术,2014,10(5):103.[doi:10.11731/j.issn.1673-193x.2014.05.018]
　CHEN Chun.Research on influencing factors of road traffic accidents——empirical study based on structural equation model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(4):103.[doi:10.11731/j.issn.1673-193x.2014.05.018]
[2]张芳燕,牛玺荣.2000~2013年我国公路交通安全研究进展及其分析[J].中国安全生产科学技术,2015,11(4):173.[doi:10.11731/j.issn.1673-193X.2015.04.028]
　ZHANG Fang-yan,NIU Xi-rong.Research progress and analysis on highway traffic Safety in China during 2000 to 2013[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2015,11(4):173.[doi:10.11731/j.issn.1673-193X.2015.04.028]
[3]高岩,董宪元,田飞.高速公路交通安全现状分析及管理对策[J].中国安全生产科学技术,2015,11(10):110.[doi:10.11731/j.issn.1673-193x.2015.10.019]
　GAO Yan,DONG Xian-yuan,TIAN Fei.Present situation analysis on highway traffic safety and management countermeasures[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2015,11(4):110.[doi:10.11731/j.issn.1673-193x.2015.10.019]
[4]吴玲,路巧珍,朱彤,等.视觉记忆型次任务对驾驶绩效及安全的影响[J].中国安全生产科学技术,2016,12(5):158.[doi:10.11731/j.issn.1673-193x.2016.05.027]
　WU Ling,LU Qiaozhen,ZHU Tong,et al.Influence of visual memory secondary task on driving performance and safety[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2016,12(4):158.[doi:10.11731/j.issn.1673-193x.2016.05.027]
[5]赵亮,刘浩学.驾驶行为特性与人格特征关系研究[J].中国安全生产科学技术,2016,12(8):171.[doi:10.11731/j.issn.1673-193x.2016.08.029]
　ZHAO Liang,LIU Haoxue.Study on relationship between driving behavior characteristics and personality traits[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2016,12(4):171.[doi:10.11731/j.issn.1673-193x.2016.08.029]
[6]刘君,胡伟超,孙广林.公路突发事件应急预案自动生成系统开发及应用[J].中国安全生产科学技术,2017,13(10):53.[doi:10.11731/j.issn.1673-193x.2017.10.009]
　LIU Jun,HU Weichao,SUN Guanglin.Development and application of automatic generation system for emergency plan of highway emergency[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2017,13(4):53.[doi:10.11731/j.issn.1673-193x.2017.10.009]
[7]王宗直,路堃,陈涛,等.基于格子气模型的北京地铁站内人流移动分析[J].中国安全生产科学技术,2018,14(5):31.[doi:10.11731/j.issn.1673-193x.2018.05.005]
　WANG Zongzhi,LU Kun,CHEN Tao,et al.Analysis on pedestrian flow movement in a subway station of Beijing based on lattice gas model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(4):31.[doi:10.11731/j.issn.1673-193x.2018.05.005]
[8]赵俊玮,华珺,刘永涛,等.不同警告方式下驾驶人应激反应能力模拟试验研究[J].中国安全生产科学技术,2019,15(6):162.[doi:10.11731/j.issn.1673-193x.2019.06.026]
　ZHAO Junwei,HUA Jun,LIU Yongtao,et al.Simulation test study on stress response ability of drivers under different warning modes[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(4):162.[doi:10.11731/j.issn.1673-193x.2019.06.026]
[9]王羽尘,马健霄,陆涛,等.公路隧道火灾发生位置与人群疏散通道仿真研究[J].中国安全生产科学技术,2019,15(10):38.[doi:10.11731/j.issn.1673-193x.2019.10.006]
　WANG Yuchen,MA Jianxiao,LU Tao,et al.Simulation study on fire location and crowd evacuation route in highway tunnel[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(4):38.[doi:10.11731/j.issn.1673-193x.2019.10.006]
[10]胡立伟,赵雪亭,杨锦青,等.城市快速过境通道衔接节点交通风险耦合致因模型研究[J].中国安全生产科学技术,2019,15(12):150.[doi:10.11731/j.issn.1673-193x.2019.12.024]
　HU Liwei,ZHAO Xueting,YANG Jinqing,et al.Research on coupling cause model of traffic risk in connecting nodes of urban rapid transit channels[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(4):150.[doi:10.11731/j.issn.1673-193x.2019.12.024]

备注/Memo

备注/Memo:
收稿日期： 2024-12-27
* 基金项目： 公安部部级课题(2022JSZ16)；广东省教育厅项目(2023KQNCX050)；广东警官学院校级课题项目(2024YBZB03)
作者简介： 胡焱松，博士研究生，主要研究方向为交通安全，交通事故分析及预防，道路交通事故处理。
通信作者： 王长君，硕士，研究员，主要研究方向为交通安全，交通事故分析及预防，自动驾驶安全性测试与分析。
更新日期/Last Update: 2025-04-28