|Table of Contents|

Influencing factors analysis and prediction of urban road traffic accident patterns(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年4期
Page:
98-107
Research Field:
交通工程
Publishing date:

Info

Title:
Influencing factors analysis and prediction of urban road traffic accident patterns
Author(s):
CHEN Li1;  LI Cong-ying2;  ZHAN Li2;  TAN Qian2;  TIAN Xin-mei2; 3; CHENG Hua4;  LI Kun2
(1. Department of Information Network, Chang'an University, Xi'an 710064, Shaanxi, China; 2. School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China; 3. China Academy of Urban Planning and Design, Bejing 100044, China; 4. Xi'an Municipal Design Research Institute Co., Ltd, Xi'an 710068, Shaanxi, China)
Keywords:
traffic engineering;  accident pattern;  prediction model;  C5.0 decision tree;  rule set
PACS:
U491.31
DOI:
10.19721/j.cnki.1671-8879.2022.04.010
Abstract:
In order to explore the causes of urban road traffic accidents, according to different traffic accident forms, the influencing factors of accidents were screened and reduced, and three different algorithms were selected to analyze and compare the accident patterns with the prediction models. The rough set theory was used to transfer and simplify the original traffic accident data, the experimental data that meet the modeling requirements was obtained. The data was equally divided into the training set and test set according to the principle of consistent overall characteristics. Based on the C5.0 decision tree algorithm, the traffic accident prediction model was constructed, and the accuracy of the model was verified, the traffic accident pattern rule set was generated. In addition, the likelihood ratio test was used to screen the independent variables to construct the multiple logistic regression model, and the multilayer perception(MLP)neural network traffic accident pattern prediction model was constructed to test the accuracy of the model training set and test set. The results show that among the three models, the accuracy of C5.0 decision tree model of training set and test set is the highest, 80.39% and 79.63% respectively, the accuracy of C5.0 decision tree model in predicting traffic accident pattern is higher than the multivariate Logistic model and MLP neural network model. Through the C5.0 decision tree model,the main influencing factors of traffic accidents patterns were got as selection of traffic mode, driving at the cross-section of the road, illegal behavior and driving status. The research provide a method for predicting the patterns of urban road traffic accidents and analyzing the causes of accidents, and provide decision-making basis for traffic management departments.10 tabs, 1 fig, 27 refs.

References:

[1] 任宇立.道路交通安全现状及交通事故分析方法初探[J].道路交通与安全,2010,10(2):4-6,3.
REN Yu-li.Road traffic security status and a study on the way of traffic accident analysis[J].Road Traffic & Safety,2010,10(2):4-6,3.
[2]刘海珠.道路交通事故严重程度影响因素分析及预测模型建立[D].长春:吉林大学,2014.
LIU Hai-zhu.The analysis of influencing factors for crash severity and the establishment of predication model[D].Changchun:Jilin University,2014.
[3]沙海云,陈彦美,柴 干.公路交通事故黑点分析系统的设计[J].中国安全科学学报,2007,17(10):75-80,180.
SHA Hai-yun,CHEN Yan-mei,CHAI Gan.Design on cause analysis system for road black spots[J].China Safety Science Journal(CSSJ),2007,17(10):75-80,180.
[4]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.
[5]AL-GHAMDI A S.Using logistic regression to estimate the influence of accident factors on accident severity[J].Accident Analysis & Prevention,2002,34(6):729-741.
[6]马壮林,邵春福,李 霞.基于Logistic模型的公路隧道交通事故严重程度的影响因素[J].吉林大学学报(工学版),2010,40(2):423-426.
MA Zhuang-lin,SHAO Chun-fu,LI Xia.Analysis of factors affecting accident severity in highway tunnels based on logistic model[J].Journal of Jilin University(Engineering and Technology Edition),2010,40(2):423-426.
[7]黄 宇,薛 锋.基于VAR模型的交通安全与经济损失关系研究[J].交通运输工程与信息学报,2021,19(1):93-99.
HUANG Yu,XUE Feng.Relationship between traffic safety and economic loss based on vector autoregressive model[J].Journal of Transportation Engineering and Information,2021,19(1):93-99.
[8]刘振博.基于ZINB及Tobit回归的高速公路交通事故预测模型研究[D].哈尔滨:哈尔滨工业大学,2019.
LIU Zhen-bo.Research on prediction models of freeway traffic accidents based on ZINB and Tobit regression[D].Harbin:Harbin Institute of Technology,2019.
[9]王晓飞,姚江贝,丁振中.三维空间路线连续性衰退对公路安全性影响分析[J].中国公路学报,2021,34(1):157-166.
WANG Xiao-fei,YAO Jiang-bei,DING Zhen-zhong.Highway safety analysis on the influence of continuous degradation of three-dimensional alignment[J].China Journal of Highway and Transport,2021,34(1):157-166.
[10]马 聪,张生瑞,马壮林,等.高速公路交通事故非线性负二项预测模型[J].中国公路学报,2018,31(11):176-185.
MA Cong,ZHANG Sheng-rui,MA Zhuang-lin,et al.Nonlinear negative binomial regression model of expressway traffic accident frequency prediction[J].China Journal of Highway and Transport,2018,31(11):176-185.
[11]ABDEL-ATY M A,ABDEL-WAHAB H T.Predicting injury severity levels in traffic crashes:A modeling comparison[J].Journal of Transportation Engineering,2004,130(2):204-210.
[12]康 迪,马寿峰,钟石泉.基于BP神经网络的微观交通安全预测方法[J].交通信息与安全,2011,29(3):79-83.
KANG Di,MA Shou-feng,ZHONG Shi-quan.A microcosmic forecasting model for traffic safety based on BP neural network[J].Journal of Transport Information and Safety,2011,29(3):79-83.
[13]孙轶轩,邵春福,赵 丹,等.交通事故严重程度C5.0决策树预测模型[J].长安大学学报(自然科学版),2014,34(5):109-116.
SUN Yi-xuan,SHAO Chun-fu,ZHAO Dan,et al.Traffic accident severity prediction model based on C5.0 decision tree[J].Journal of Chang'an University(Natural Science Edition),2014,34(5):109-116.
[14]刘伟铭,管丽萍,尹湘源.基于决策树的高速公路事件持续时间预测[J].中国公路学报,2005,18(1):99-103.
LIU Wei-ming,GUAN Li-ping,YIN Xiang-yuan.Prediction of freeway incident duration based on decision tree[J].China Journal of Highway and Transport,2005,18(1):99-103.
[15]肖向良.电动自行车道路交通安全事故严重性影响因素分析[J].公路与汽运,2020(6):32-36.
XIAO Xiang-liang.Analysis of factors affecting the severity of electric bicycle road traffic safety accidents[J].Highways & Automotive Applications,2020(6):32-36.
[16]PANDE A,ABDEL-ATY M.Market basket analysis of crash data from large jurisdictions and its potential as a decision support tool[J].Safety Science,2009,47(1):145-154.
[17]王 云,苏 勇.关联规则挖掘在道路交通事故分析中的应用[J].科学技术与工程,2008,8(7):1824-1827.
WANG Yun,SU Yong.Application of association rule in the analysis of traffic accident[J].Science Technology and Engineering,2008,8(7):1824-1827.
[18]BEZUGLOV A,COMERT G.Short-term freeway traffic parameter prediction:Application of grey system theory models[J].Expert Systems With Applications,2016,62:284-292.
[19]JUREWICZ C,EXCEL R.Application of a crash-predictive risk assessment model to prioritise road safety investment in Australia[J].Transportation Research Procedia,2016,14:2101-2110.
[20]詹 伟,吕 庆,尚岳全.高速公路隧道群交通事故灰色马尔可夫预测[J].吉林大学学报(工学版),2014,44(1):62-67.
ZHAN Wei,LYU Qing,SHANG Yue-quan.Analysis of gray-Markov forecasting for traffic accidents in highway tunnel group region[J].Journal of Jilin University(Engineering and Technology Edition),2014,44(1):62-67.
[21]曾 强,王雪松,张 璇,等.基于时空交互模型的高速公路季节事故频次影响因素分析[J].中国公路学报,2020,33(11):255-263.
ZENG Qiang,WANG Xue-song,ZHANG Xuan,et al.Seasonal analysis of contributing factors to freeway crash frequency using a spatio-temporal interaction model[J].China Journal of Highway and Transport,2020,33(11):255-263.
[22]贺 宜,杨鑫炜,吴 兵,等.中美交通事故数据统计方法比较研究[J].交通信息与安全,2018,36(1):1-9,27.
HE Yi,YANG Xin-wei,WU Bing,et al.A comparison of statistical survey methods of traffic accident data between China and the United States[J].Journal of Transport Information and Safety,2018,36(1):1-9,27.
[23]李 博,窦盼英.基于不完备信息系统的粗糙分类研究[J].计算机工程与应用,2006,42(12):190-192.
LI Bo,DOU Pan-ying.Rough classification based on incomplete information systems[J].Computer Engineering and Applications,2006,42(12):190-192.
[24]徐 袭,刘玉波,范学鑫.基于模糊工具箱和ROSETTA的粗糙集数据挖掘[J].微计算机信息,2007,23(18):174-175,178.
XU Xi,LIU Yu-bo,FAN Xue-xin.Data mining with rough set based on fuzzy toolbox and ROSETTA[J].Microcomputer Information,2007,23(18):174-175,178.
[25]谭俊璐,武建华.基于决策树规则的分类算法研究[J].计算机工程与设计,2010,31(5):1017-1019.
TAN Jun-lu,WU Jian-hua.Classification algorithm of rule based on decision-tree[J].Computer Engineering and Design,2010,31(5):1017-1019.
[26]QUINLAN J R.C4.5:Programs for machine learning[M].San Mateo:Morgan Kaufmann Publishers,1993.
[27]郑丽琴.基于数据挖掘的决策树算法和C5.0原理简介[J].知识经济,2014(7):87-88.
ZHENG Li-qin.A brief introduction of decision tree algorithm based on data mining and C5.0 principle[J].Knowledge Economy,2014(7):87-88.

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Last Update: 2022-07-20