Volume 40 Issue 2
Apr.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(2): 45-52
WEI Wen, DU Yumeng, DONG Aoran, QIN Dan, ZHU Tong. An Analysis of Factors Affecting Injury of Electric Two-wheeler Riders Based on CIDAS Data and Ensemble Learning[J]. Journal of Transport Information and Safety, 2022, 40(2): 45-52. doi: 10.3963/j.jssn.1674-4861.2022.02.006
Citation: WEI Wen, DU Yumeng, DONG Aoran, QIN Dan, ZHU Tong. An Analysis of Factors Affecting Injury of Electric Two-wheeler Riders Based on CIDAS Data and Ensemble Learning[J]. Journal of Transport Information and Safety, 2022, 40(2): 45-52. doi: 10.3963/j.jssn.1674-4861.2022.02.006
shu

An Analysis of Factors Affecting Injury of Electric Two-wheeler Riders Based on CIDAS Data and Ensemble Learning

doi: 10.3963/j.jssn.1674-4861.2022.02.006

  • College of Transportation Engineering, Chang'an University, Xi'an 710064, China

  • Received Date: 2021-12-20
    Available Online: 2022-05-18
    Abstract
  • A growing use of electric two-wheelers leads to an increasing number of serious accidents. In order to study the factors affecting injury severity of electric two-wheeler riders within the collisions involving electric two-wheelers, three integrated learning models, i.e. random forest, XGBoost, and LightGBM, are developed and compared based on 1 246 electric two-wheelers and motor vehicle accidents collected from the China Depth of Accident Investigation(CIDAS)dataset. According to the accuracy and other indicators, the LightGBM model is chosen for its best performance to predict the severity of injury suffered by electric vehicle riders. With SHAP-method analysis, a nonlinear relationship between independent variables and dependent variables is observed. There is an evident threshold for the impacts of the throwing distance of the electric two-wheeler riders on the risk of death. Electric two-wheeler riders are not susceptible to death accidents when the throwing distance is less than 5 meters. When the throwing distance exceeds 5 meters, there is a positively correlation between throwing distance and risk of death. Accidents occur in outside urban areas or on highways and collisions with heavy vehicles significantly increase the risk of death to riders involved in accidents. Factors like no pedal, seat height greater than 70 cm, handlebar width of 61~65 cm, and handlebar design of backward bending or horn shape can reduce the risk of death. Being female, age 30~50, and familiar with the location are associated with a lower risk of death.

     

    • FullText(HTML)
  • loading
    • References(21)
  • [1]
    PATRIZIA H, ANDREA U, STEFFEN N, et al. Characteristics of single vehicle crashes with e-bikes in Switzerland[J]. Accident Analysis & Prevention, 2018, 117(4): 232-238.
    [2]
    马国忠, 明士军, 吴海涛. 电动自行车安全特性分析[J]. 中国安全科学学报, 2006, 16(4): 48-52. doi: 10.3969/j.issn.1003-3033.2006.04.009

    MA G Z, MING S J, WU H T. On safety character of electric bicycle[J]. China Safety Science Journal, 2006, 16(4): 48-52. (in Chinese) doi: 10.3969/j.issn.1003-3033.2006.04.009
    [3]
    王卫杰, 沈轩霆, 王贵彬, 等. 电动自行车骑行者事故伤害程度影响因素分析[J]. 中国安全科学学报, 2019, 29(2): 20-25. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201902004.htm

    WANG W J, SHEN X T, WANG G B, et al. Analysis of factors affecting injury to electric bi-cycle rider in crash[J]. China Safety Science Journal, 2019, 29(2): 20-25. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201902004.htm
    [4]
    江亮, 贺宜. 电动两轮车风险驾驶行为及事故影响因素分析[J]. 吉林大学学报(工学版), 2019, 49(4): 1107-1113. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201904011.htm

    JIANG L, HE Y. Risky driving behavior and influencing factors analysis for electric two-wheeler[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1107-1113. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201904011.htm
    [5]
    李英帅, 张旭, 王卫杰, 等. 基于随机森林的电动自行车骑行者事故伤害程度影响因素分析[J]. 交通运输系统工程与信息, 2021, 21(1): 196-200. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202101032.htm

    LI Y S, ZHANG X, WANG W J, et al. Factors affecting electric bicycle rider injury in accident based on random forest model[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(1): 196-200. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202101032.htm
    [6]
    PARSA A B, TAGHIPOUR H, DERRIBLE S, et al. Real-time accident detection: Coping with imbalanced data[J]. Accident Analysis & Prevention, 2019, 129(8): 202-210.
    [7]
    BAO J, LIU P, UKKUSURI S V. A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data[J]. Accident Analysis & Prevention, 2019, 122(1): 239-254.
    [8]
    张文婧, 陈治亚, 冯芬玲, 等. 基于稀疏理论的DAE在公路事故伤亡预测应用[J]. 计算机工程与应用, 2019, 55(7): 241-247. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201907038.htm

    ZHANG W J, CHEN Z Y, FENG F L, et al. Application of deep auto-encoder based on sparse theory in highway accident casualty forecast[J]. Computer Engineering and Applications, 2019, 55(7): 241-247. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201907038.htm
    [9]
    柳本民, 闫寒. 基于SVM事故分类的连环追尾事故影响因素分析[J]. 交通信息与安全, 2020, 38(1): 43-51. doi: 10.3963/j.jssn.6174-4861.2020.01.006

    LIU B M, YAN H. An analysis of influencing factors of multi-vehicle rear-end accidents based on accident classification of SVM[J]. Journal of Transport Information and Safety, 2020, 38(1): 43-51. (in Chinese) doi: 10.3963/j.jssn.6174-4861.2020.01.006
    [10]
    WEN H Y, ZHANG X, ZENG Q, et al. Predicting future driving risk of crash-involved drivers based on a systematic machine learning framework[J]. International Journal of Environmental Research and Public Health, 2019, 16(3): 334-352. doi: 10.3390/ijerph16030334
    [11]
    纪俊红, 昌润琪, 温廷新. 基于GSK-AdaBoost-LightGBM的交通事故死亡人数预测研究[J]. 安全与环境工程, 2021, 28(1): 24-28. https://www.cnki.com.cn/Article/CJFDTOTAL-KTAQ202101004.htm

    JI J H, CHANG R Q, WEN T X. Prediction of traffic accident death toll based on GSK-AdaBoost-LightGBM[J]. Safety and Envioronmental Engineering, 2021, 28(1): 24-28. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KTAQ202101004.htm
    [12]
    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(8): 106153.
    [13]
    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(1): 321-357.
    [14]
    KE G L, MENG Q, FINLEY T, et al. Lightgbm: A highly efficient gradient boosting decision tree[J]. Advances in Neural Information Processing Systems, 2017, 30(1): 3146-3154.
    [15]
    王芳杰, 王福建, 王雨晨, 等. 基于LightGBM算法的公交行程时间预测[J]. 交通运输系统工程与信息, 2019, 19(2): 116-121. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201902017.htm

    WANG F J, WANG F J, WANG Y C, et al. Bus travel time prediction based on light gradient boosting machine algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(2): 116-121. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201902017.htm
    [16]
    BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32. doi: 10.1023/A:1010933404324
    [17]
    CHEN T Q, GUESTRIN C. Xgboost: A scalable tree boosting system[C]. The 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA: Association for Computing Machinery, 2016.
    [18]
    KIDANDO E, MOSES R, OZGUVEN E E, et al. Incorporating travel time reliability in predicting the likelihood of severe crashes on arterial highways using non-parametric random effect regression[J]. Journal of Traffic and Transportation Engineering(English edition), 2019, 6(5): 470-481. doi: 10.1016/j.jtte.2018.04.003
    [19]
    中华人民共和国工业和信息化部. 电动自行车安全技术规范: GB 17761—2018[S]. 北京: 中国标准出版社, 2018.

    Ministry of Industry and Information Technology, People's Republic of China. Safety technical specification for electric bicycle: GB 17761—2018[S]. Beijing: Standards Press of China, 2018. (in Chinese)
    [20]
    WANG T, CHEN J, SHEN X J. CICTP 2014: Safe, Smart, and Sustainable Multimodal Transportation Systems: Proceedings of the 14th COTA International Conference of Transportation Professionals[M]. Reston, Virginia, USA: American Society of Civil Engineers, 2014.
    [21]
    陈昭明, 徐文远, 曲悠扬, 等. 基于混合Logit模型的高速公路交通事故严重程度分析[J]. 交通信息与安全, 2019, 37 (3): 42-50. doi: 10.3963/j.issn.1674-4861.2019.03.006

    CHEN Z M, XU W Y, QU Y Y, et al. Severity of traffic crashes on freeways based on mixed logit model[J]. Journal of Transport Information and Safety, 2019, 37(3): 42-50. (in Chinese) doi: 10.3963/j.issn.1674-4861.2019.03.006
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(5)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (954) PDF downloads(59) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return