Volume 41 Issue 5
Oct.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(5): 43-53
WANG Chaojian, ZHANG Daowen, JIANG Jun, XIAO Le. An Analysis of Fatal Accident Rates of Passenger Cars on Urban Roads Considering Imbalanced Data Samples[J]. Journal of Transport Information and Safety, 2023, 41(5): 43-53. doi: 10.3963/j.jssn.1674-4861.2023.05.005
Citation: WANG Chaojian, ZHANG Daowen, JIANG Jun, XIAO Le. An Analysis of Fatal Accident Rates of Passenger Cars on Urban Roads Considering Imbalanced Data Samples[J]. Journal of Transport Information and Safety, 2023, 41(5): 43-53. doi: 10.3963/j.jssn.1674-4861.2023.05.005
shu

An Analysis of Fatal Accident Rates of Passenger Cars on Urban Roads Considering Imbalanced Data Samples

doi: 10.3963/j.jssn.1674-4861.2023.05.005
  • 1.

    School of Automobile and Transportation, Xihua University, Chengdu 610039, China

  • 2.

    School of Engineering and Technology, Sichuan Sanhe College of Professionals, Luzhou 646200, Sichuan, China

  • 3.

    Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu 610039, China

  • 4.

    Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan, Xihua University, Chengdu 610039, China

  • Received Date: 2022-03-29
    Available Online: 2024-01-18
    Abstract
  • Traffic accidents on urban roads are frequent, and there is a significant imbalance in accident data. The coupling between different factors caused great challenges in analyzing the fatal accident rate of passenger vehicles on urban roads. Therefore, a three-stage method that integrating resampling, Bayesian networks (BN) and association rule method (ARM) is proposed. Based on the data of 1105 urban road passenger car accidents from the National Automobile Accident In-Depth Investigation System (NAIS), the BN model is constructed by selecting 16 potential feature variables from four aspects: driver, vehicle, roadway and environment. Considering the problem that the imbalance of accident types can lead to the degradation performance of BN model. Proposed data re-sampling using Synthetic Minority Over-sampling Technique (SMOTE) and Cluster Centroids (CC) before the construction of BN model. Compare the comprehensive performance of different BN models under various sampling techniques. Finally, based on the optimal BN model and combined with the ARM, the effects of different influencing factors and the coupling effect of factors on the fatal accident rate were analyzed. The results show that re-sampling method can significantly improve the comprehensive performance of BN models and the ability to identify risk factors. Among them, the BN model constructed by SMOTE sampling technique combined with GTT algorithm has the highest AUC of 0.793. Besides, compared with the BN model constructed by the original imbalanced data, the BN model constructed by SMOTE sampling explores six more risk factors. The highest fatal accident rate was 80.4% when "motorized two/three-wheelers"are coupled with"speeding". The next highest fatal accident rate is 77.4% when "motorized two/three wheelers"is coupled with"blind spots in the field of vision". Passenger cars are prone to crash with cars when turning left at the Four-Way Intersection, but the fatal accident rate is less than 20%. This method can reduce the influence of data imbalance on the analysis of road traffic accidents, and realize the analysis of the coupling effect of risk factors, thus preventing and reducing the occurrence of fatal accidents on urban roads.

     

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    • References(32)
  • [1]
    刘爱华, 叶植材. 中国统计年鉴—2020[M]. 北京: 中国统计出版社, 2020.

    LIU A H, YE Z C. China statistical yearbook – 2020[M]. Beijing: China Statistics Press, 2020. (in Chinese)
    [2]
    胡宗品, 骆仁佳, 于淑君. 城市死亡交通事故形态影响因素分析[J]. 交通科技与经济, 2022, 24(1): 25-29, 37.

    HU Z P, LUO R J, YU S J. Analysis of influencing factors on urban fatal crash types[J]. Technology & Economy in Areas of Communications, 2022, 24(1): 25-29, 37. (in Chinese)
    [3]
    赵琳娜, 贾兴无, 戴帅, 等. 中国城市道路交通安全特点解析[J]. 城市交通, 2018, 16(3): 9-14, 20.

    ZHAO L N, JIA X W, DAI S, et al. Characteristics of urban road traffic safety in China[J]. Urban Transport of China, 2018, 16(3): 9-14, 20. (in Chinese)
    [4]
    张道文, 母尧尧, 王朝健, 等. 城市道路交通事故特性及严重程度研究[J]. 安全与环境学报, 2022, 22(2): 599-605.

    ZHANG D W, MU Y Y, WANG C J, et al. Research on characteristics and severity of urban road traffic accidents[J]. Journal of Safety and Environment, 2022, 22(2): 599-605. (in Chinese)
    [5]
    FOUNTAS G, ANASTASOPOULOS P C. Analysis of accident injury-severity outcomes: the zero-inflated hierarchical ordered probit model with correlated disturbances[J]. Analytic Methods in Accident Research, 2018, 20: 30-45. doi: 10.1016/j.amar.2018.09.002
    [6]
    WEN X, XIE Y, JIANG L, et al. Applications of machine learning methods in traffic crash severity modelling: current status and future directions[J]. Transport Reviews, 2021, 41(6): 855-879. doi: 10.1080/01441647.2021.1954108
    [7]
    MORAL-GARCÍA S, CASTELLANO J G, MANTAS C J, et al. Decision tree ensemble method for analyzing traffic accidents of novice drivers in urban areas[J]. Entropy, 2019, 21(4): 360. doi: 10.3390/e21040360
    [8]
    SHAIK M E, ISLAM M M, HOSSAIN Q S. A review on neural network techniques for the prediction of road traffic accident severity[J]. Asian Transport Studies, 2021, 7: 100040. doi: 10.1016/j.eastsj.2021.100040
    [9]
    吕通通, 张湛, 陆林军, 等. 基于互信息贝叶斯网络的交通事故严重程度分析[J]. 交通信息与安全, 2021, 39(6): 36-43. doi: 10.3963/j.jssn.1674-4861.2021.06.005

    LYU T T, ZHANG Z, LU L J, et al. An analysis of traffic accident severity based on mutual-information Bayesian net-work[J]. Journal of Transport Information and Safety, 2021, 39(6): 36-43. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.06.005
    [10]
    李贵阳, 张福明, 王永岗. 基于SVM模型的山区高速公路多车事故影响因素分析[J]. 武汉理工大学学报(交通科学与工程版), 2020, 44(6): 1046-1051. doi: 10.3963/j.issn.2095-3844.2020.06.020

    LI G Y, ZHANG F M, WANG Y G, et al. Influencing factors analysis of multiple vehicle accidents in mountainous expressway based on SVM mode[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2020, 44(6): 1046-1051. (in Chinese) doi: 10.3963/j.issn.2095-3844.2020.06.020
    [11]
    VILAÇA M, MACEDO E, COELHO M C. A rare event modelling approach to assess injury severity risk of vulnerable road users[J]. Safety, 2019, 5(2): 29. doi: 10.3390/safety5020029
    [12]
    方方, 王昕. 基于集成学习的不平衡交通事故风险研究[J]. 北京信息科技大学学报(自然科学版), 2021, 36 (06): 19-24.

    FANG F, WANG X. Research on unbalanced traffic accident risk based on ensemble learning[J]. Journal of Beijing Information Science & Technology University, 2021, 36(06): 19-24. (in Chinese)
    [13]
    恽天翔. 基于机器学习的道路交通事故严重程度分析和预测[D]. 南京: 南京师范大学, 2021.

    YUN T X. Road traffic accident severity Analysis and prediction based on machine learning[D]. Nanjing: Nanjing Normal University, 2021. (in Chinese)
    [14]
    YANG C, CHEN M, YUAN Q. The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: an exploratory analysis[J]. Accident Analysis & Prevention, 2021, 158: 106153.
    [15]
    束鹍. 基于可解释机器学习的城市道路交通事故严重程度预测[D]. 西安: 长安大学, 2021.

    SHU K. Analysis of factors contributing to crash severity on urban road based on explainable machine learning[D]. Xi'an: Chang'an University, 2021. (in Chinese)
    [16]
    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): 150-155.

    HU L W, ZHAO X T, YANG J Q, 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(12): 150-155. (in Chinese)
    [17]
    魏珊珊. 基于数据挖掘的危险货物道路运输事故机理研究[D]. 西安: 长安大学, 2021.

    WEI S S. Application of data mining for the mechanism of hazardous materials road transport accidents[D]. Xi'an: Chang'an University, 2021. (in Chinese)
    [18]
    刘晨. 基于NAIS的交通伤严重程度影响因素研究[D]. 北京: 清华大学, 2015.

    LIU C. Study of influencing factors on traffic injury severity based on the NAIS[D]. Beijing: Tsinghua University, 2015. (in Chinese)
    [19]
    陈彬, 于鹏程, 张奇. 基于Apriori算法的特殊路段事故致因关联规则挖掘研究[J]. 道路交通管理, 2022(3): 34-37.

    CHEN B, YU P C, ZHANG Q. Research on Mining cause-related association rules of special road accidents based on Apriori algorithm[J]. Road Traffic Control, 2022(3): 34-37. (in Chinese)
    [20]
    THAMMASIRI D, DELEN D, MEESAD P, et al. A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition[J]. Expert Systems with Applications, 2014, 41(2): 321-330 doi: 10.1016/j.eswa.2013.07.046
    [21]
    李蒙蒙, 刘艺, 李庚松, 等. 不平衡多分类算法综述[J/OL]. 计算机应用: 1-17[2022-05-24]. http://kns.cnki.net/kc-ms/detail/51.1307.TP.20220221.1455.002.html.

    LI M M, LIU Y, LI G S, et al. Survey on imbalanced multi-class classification algorithms[J/OL]. Journal of Computer Applications: 1-17[2022-05-24]. http://kns.cnki.net/kcms/detail/51.1307.TP.20220221.1455.002.html. (in Chinese)
    [22]
    DELEN D, TOMAK L, TOPUZ K, et al. Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods[J]. Journal of Transport & Health, 2017(4): 118-131.
    [23]
    CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of artificial intelligence research, 2002, 16: 321-357. doi: 10.1613/jair.953
    [24]
    任化娟. 面向不平衡数据的分类方法研究[D]. 郑州: 郑州大学, 2020.

    REN H J. Research on methods for classifying imbalanced data[D]. Zhengzhou: Zhengzhou University. (in Chinese)
    [25]
    MENG H, AN X, XING J. A data-driven Bayesian network model integrating physical knowledge for prioritization of risk influencing factors[J]. Process Safety and Environmental Protection, 2022, 160: 434-449.
    [26]
    谢小慧. 基于的水质评价及水质因子关联性分析[D]. 成都: 西南交通大学, 2019.

    XIE X H. WATER Quality evaluation and correlation analysis of indicators based on bayesian network[D]. Chengdu: Southwest Jiaotong University, 2019. (in Chinese)
    [27]
    董傲然, 王长帅, 秦丹, 等. 机动车-行人事故中行人伤害严重程度分析[J]. 中国安全科学学报, 2020, 30(11): 141-147.

    DONG A R, WANG C S, QIN D, et al. Analysis on injury severity of pedestrian in motor vehicle-pedestrian accidents[J]. China Safety Science Journal, 2020, 30(11): 141-147. (in Chinese).
    [28]
    王精滢. 考虑空间异质性的机非交通事故严重程度分析[D]. 成都: 西南交通大学, 2020.

    WANG J Y. Severity analysis of motorized and non-motorized vehicle crashes considering spatial heterogeneity[D]. Chengdu: Southwest Jiaotong University, 2020. (in Chinese).
    [29]
    王琳琳. 交叉口交通事故损伤严重程度与影响因素分析[D]. 青岛: 山东科技大学, 2020.

    WANG L L. Analysis on injury severity and influencing factors of traffic accidents at intersections[D]. Qingdao: Shandong University of Science and Technology, 2020. (in Chinese).
    [30]
    LI Z, WU Q, CI Y, et al. Using latent class analysis and mixed logit model to explore risk factors on driver injury severity in single-vehicle crashes[J]. Accident Analysis & Prevention, 2019, 129: 230-240.
    [31]
    张礼宁. 典型天气条件下山地城市道路异常驾驶行为研究[D]. 重庆: 重庆交通大学, 2021.

    ZHANG L L. Research on abnormal driving behavior of mountain city road under typical weather conditions[D]. Chongqin: Chongqing Jiaotong University, 2021. (in Chinese)
    [32]
    LEEVY J L, KHOSHGOFTAAR T M, BAUDER R A, et al. A survey on addressing high-class imbalance in big data[J]. Journal of Big Data, 2018, 5(1): 1-30
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