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 |
[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
|