|本期目录/Table of Contents|

[1]韩天园,吕凯光,许江超,等.基于APRIORI-TAN的交通事故伤害分析与预测*[J].中国安全生产科学技术,2021,17(8):50-56.[doi:10.11731/j.issn.1673-193x.2021.08.008]
　HAN Tianyuan,LYU Kaiguang,XU Jiangchao,et al.Analysis and prediction of traffic accident injury based on APRIORI-TAN[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(8):50-56.[doi:10.11731/j.issn.1673-193x.2021.08.008]
点击复制

基于APRIORI-TAN的交通事故伤害分析与预测*
分享到：

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

卷:
17
期数:
2021年8期
页码:
50-56
栏目:
学术论著
出版日期:
2021-08-31

文章信息/Info

Title:
Analysis and prediction of traffic accident injury based on APRIORI-TAN
文章编号:
1673-193X(2021)-08-0050-07
作者:
韩天园; 吕凯光; 许江超; 李旋; 乔洁
（长安大学 汽车学院，陕西 西安 710064）
Author(s):
HAN Tianyuan;  LYU Kaiguang;  XU Jiangchao;  LI Xuan;  QIAO Jie
(School of Automobile,Chang’an University,Xi’an Shaanxi 710064,China)
关键词:
交通安全; 事故伤害; 关联规则; 社会网络分析; 树型贝叶斯网络; 伤害预测
Keywords:
traffic safety;  accident injury;  association rule;  social network analysis;  tree Bayesian network (TAN);  injury prediction
分类号:
X928.03
DOI:
10.11731/j.issn.1673-193x.2021.08.008
文献标志码:
A
摘要:
为探究道路交通事故因素和事故伤害的相关性，以2 467起涉及人员伤亡的交通事故为数据集，运用Apriori算法分别挖掘事故伤害关联规则，并结合社会网络分析的可视化和核心-边缘分析构建受伤事故和死亡事故的关联规则网络。结果表明：事故伤害程度与事故时间、道路条件和交通环境等因素关系紧密，尤其死亡事故与碰撞固定物、人行横道事故、高速公路、高速道路、非市区、酒驾和超速存在高相关性。基于树型贝叶斯网络（TAN）构建事故伤害程度的预测模型，预测结果准确率可达87.56%。
Abstract:
In order to explore the correlation between road traffic accident factors and accident injuries,taking 2 467 traffic accidents involving casualties as the data set,the Apriori algorithm was used to mine the association rules of accident injury respectively,and the association rule network of injury accidents and fatal accidents was constructed combined with the visualization of social network analysis and core-edge analysis.The results showed that the degree of accident injury was closely related to the factors such as accident time,road conditions and traffic environment,etc.In particular,there was a high correlation between the fatal accidents and collision with fixed objects,crosswalk accidents,expressways,high-speed roads,non-urban areas,drunk driving and speeding.Finally,a prediction model of accident injury degree was constructed based on the tree Bayesian network (TAN),and the accuracy of prediction results could reach 87.56%.

参考文献/References:

［1］WANG D,LIU Q,MA L,et al.Road traffic accident severity analysis:a census-based study in China［J］.Journal of Safety Research,2019,70:135-147.
［2］WANG L,NING P,YIN P,et al.Road traffic mortality in China:analysis of national surveillance data from 2006 to 2016［J］.The Lancet Public Health,2019,4(5):e245-e255.
［3］ZHANG G,KELVIN Y,CHEN G.Risk factors associated with traffic violations and accident severity in China［J］.Accident Analysis & Prevention,2013,59:18-25.
［4］SUN L,LIU D,CHEN T,et al.Road traffic safety:an analysis of the cross-effects of economic,road and population factors［J］.Chinese Journal of Traumatology,2019,22(5):290-295.
［5］赵树恩,屈贤,张金龙.基于人车路协同的车辆弯道安全车速预测［J］.汽车工程,2015,37(10):1208-1214,1220. ZHAO Shuen,QU Xian,ZHANG Jinlong.Prediction of safe vehicle speed on curved roads based on driver-vehicle-road collaboration［J］.Automotive Engineering,2015,37(10):1208-1214,1220.
［6］BATTIATO S,FARINELLA G M,GALLO G,et al.On-board monitoring system for road traffic safety analysis［J］.Computers in Industry,2018,98:208-217.
［7］KAUR G,KAUR H.Prediction of the cause of accident and accident prone location on roads using data mining techniques［C］//2017 8th International Conference on Computing,Communication and Networking Technologies (ICCCNT).Piscataway:IEEE,2017:1-7.
［8］TAAMNEH M,ALKHEDER S,TAAMNEH S.Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates［J］.Journal of Transportation Safety & Security,2017,9(2):146-166.
［9］刘祖德,刘永泰,王淸淸.相关关系和因果关系在事故分析中的应用——研究综述与启示［J］.安全与环境学报,2020,20(1):169-177. LIU Zude,LIU Yongtai,WANG Qingqing.Applications of the relevant and cause-result relationship to the analysis of the production safety accidents:summary and enlightenment［J］.Journal of Safety and Environment,2020,20(1):169-177.
［10］LAMR M.Big data and its usage in systems of early warning of traffic accident risks［C］//2018 Sixth International Conference on Enterprise Systems (ES).Piscataway:IEEE,2018:154-157.
［11］XIA X,NAN B,XU C.Real-time traffic accident severity prediction using data mining technologies［C］//2017 International Conference on Network and Information Systems for Computers (ICNISC).Piscataway:IEEE,2017:242-245.
［12］GUTIERREZ-OSORIO C,PEDRAZA C.Modern data sources and techniques for analysis and forecast of road accidents:a review［J］.Journal of Traffic and Transportation Engineering (English Edition),2020,7(4):432-446.
［13］RUSLI R,HAQUE M M,SAIFUZZAMAN M,et al.Crash severity along rural mountainous highways in Malaysia:an application of a combined decision tree and logistic regression model［J］.Traffic Injury Prevention,2018,19(7):741-748.
［14］XU X,YAN S,WANG Y.Researching on traffic accident based on relevance analysis［C］//2019 IEEE International Conference on Power,Intelligent Computing and Systems (ICPICS).Piscataway:IEEE,2019:629-632.
［15］ALKHEDER S,ALRUKAIBI F,AIASH A.Risk analysis of traffic accidents’ severities:an application of three data mining models［J］.ISA Transactions,2020,106:213-220.

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(8):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(8):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(8):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(8):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(8):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(8):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(8):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(8):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(8):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(8):150.[doi:10.11731/j.issn.1673-193x.2019.12.024]

备注/Memo

备注/Memo:
收稿日期： 2021-01-31
* 基金项目： 陕西省重点研发计划项目（2020ZDLGY16-08）；教育部人文社会科学青年基金项目（18YJCZH110）
作者简介： 韩天园，硕士研究生，主要研究方向为道路交通事故大数据挖掘。
通信作者： 乔洁，博士，工程师，主要研究方向为人车路系统安全。
更新日期/Last Update: 2021-09-08