China Safety Science Journal ›› 2023, Vol. 33 ›› Issue (4): 155-162.doi: 10.16265/j.cnki.issn1003-3033.2023.04.0826
• Public safety • Previous Articles Next Articles
CHEN Jiaona1,2(), JIN Yinli3, TAO Weijun1, LI Daofeng1
Received:
2022-11-19
Revised:
2023-02-08
Online:
2023-04-28
Published:
2023-10-28
CHEN Jiaona, JIN Yinli, TAO Weijun, LI Daofeng. Research on mediating effect of express way accident duration based on text information[J]. China Safety Science Journal, 2023, 33(4): 155-162.
Add to citation manager EndNote|Ris|BibTeX
URL: http://www.cssjj.com.cn/EN/10.16265/j.cnki.issn1003-3033.2023.04.0826
Tab.2
Definition of model variables
变量类别 | 变量 | 说明 |
---|---|---|
因变量Y | 持续时间Y | 报警时刻与处置完成时刻之差 |
自变量X | 月份X1 | 事件报警时刻所属的月份,1—12 |
时段X2 | 事件报警时刻所属的时段,0∶00—23∶00 | |
星期X3 | 1星期一;2星期二;3星期三;4星期四;5星期五;6星期六;7星期日 | |
事故类型X4 | 1单方侧翻;2单方碰撞;3单方自燃;4单方故障;5多方追尾;6多方相撞;7多方剐蹭 | |
事故范围X5 | 开始桩号与结束桩号之差 | |
天气X6 | 1晴;2阴;3雨;4雪;5雾 | |
位置类型X7 | 1道路;2主线;3收费站; 4桥梁;5隧道 | |
控制变量C | 受伤人数C1 | — |
死亡人数C2 | — | |
损毁车辆数C3 | — | |
危化品车辆C4 | 是否涉及危化品车辆:1涉及;0不涉及 | |
中介变量M(待检验) | 上报次数M1 | 事件持续期间信息报送次数 |
字符数M2 | 事件发现时首次文本信息报送字符数 | |
情报板发布M3 | 事件发现时是否在全线情报板进行发布:0否,1是 | |
主题关键词Mj | — |
Tab.4
Analysis of the intermediary role of information submission
路径 | 检验结论 | 效应占比/% |
---|---|---|
月份→字符数→持续时间 | 遮掩效应 | 7.296 |
月份→上报次数→持续时间 | 不显著 | 0 |
月份→情报板发布→持续时间 | 不显著 | 0 |
时段→字符数→持续时间 | 部分中介 | 7.075 |
时段→上报次数→持续时间 | 不显著 | 0 |
时段→情报板发布→持续时间 | 不显著 | 0 |
星期→字符数→持续时间 | 不显著 | 0 |
星期→上报次数→持续时间 | 不显著 | 0 |
星期→情报板发布→持续时间 | 不显著 | 0 |
天气→字符数→持续时间 | 不显著 | 0 |
天气→上报次数→持续时间 | 完全中介 | 100 |
天气→情报板发布→持续时间 | 不显著 | 0 |
位置→字符数→持续时间 | 不显著 | 0 |
位置→上报次数→持续时间 | 不显著 | 0 |
位置→情报板发布→持续时间 | 不显著 | 0 |
事故范围→字符数→持续时间 | 完全中介 | 100 |
事故范围→上报次数→持续时间 | 不显著 | 0 |
[1] |
doi: 10.1016/j.ecotra.2013.12.003 |
[2] |
doi: 10.1007/s12544-017-0283-3 |
[3] |
doi: 10.1016/J.AAP.2021.106322 |
[4] |
杨文臣, 张轮, 施奕骋, 等. 城市快速路交通事件持续时间生存分析[J]. 交通运输系统工程与信息, 2014, 14(5):168-174.
|
|
|
[5] |
doi: 10.1016/j.aap.2015.08.024 |
[6] |
郑先俊, 尹静, 陈瑞华, 等. 青藏高速公路危险物品运输环境风险评价及应急对策[J]. 水土保持通报, 2019, 39(3):270-275.
|
|
|
[7] |
苏磊. 基于深度数据挖掘的危险品运输事故关键影响因素及致因模型研究[D]. 青岛: 青岛科技大学, 2019.
|
|
|
[8] |
|
[9] |
|
[10] |
doi: 10.1007/s13369-019-04073-5 |
[11] |
|
[12] |
|
[13] |
|
[14] |
doi: 10.1016/j.trc.2013.10.002 |
[15] |
纪柯柯, 陈坚, 肖思瑶, 等. 文本数据驱动下的高速公路事故持续时间预测模型[J]. 交通信息与安全, 2020, 38(6):9-16.
|
|
|
[16] |
牟庆泉, 丁小兵, 刘志钢, 等. 基于地铁运营日志文本挖掘的危险源辨识算法研究[J]. 中国安全生产科学技术, 2022, 18(3):204-210.
|
|
|
[17] |
韩天园, 田顺, 吕凯光, 等. 基于文本挖掘的重特大交通事故成因网络分析[J]. 中国安全科学学报, 2021, 31(9):150-156.
doi: 10.16265/j.cnki.issn1003-3033.2021.09.021 |
doi: 10.16265/j.cnki.issn1003-3033.2021.09.021 |
|
[18] |
于卫红, 付飘云, 任月, 等. 基于PMI与BTM的船舶事故原因文本挖掘[J]. 交通信息与安全, 2021, 39(1):35-44.
|
|
|
[19] |
孙欢. 基于BERT+BiLSTM+CRF模型和改进Apriori算法的交通事故文本分析[D]. 西安: 长安大学, 2021.
|
|
|
[20] |
屠君超. 基于深度学习的交通文本检测和识别方法的研究[D]. 成都: 电子科技大学, 2021.
|
|
[1] | HE Shubo, XIANG Wei, SHI Zhongmiao. Risk early warning of electric vehicle battery system based on machine learning [J]. China Safety Science Journal, 2023, 33(2): 159-165. |
[2] | WU Xianguo, FENG Zongbao, LIU Jun, WANG Lei, CHEN Hongyu, LI Xinyi. Multi-objective optimization of surface settlement safety control during shield construction based on RF-NSGA-II [J]. China Safety Science Journal, 2022, 32(8): 45-51. |
[3] | YANG Wenchen, ZHOU Yanning, TIAN Bijiang, GUO Fengxiang, HU Chengyu. Traffic accident severity prediction for secondary highways based on cluster analysis and SVM model [J]. China Safety Science Journal, 2022, 32(5): 163-169. |
[4] | LU Ying, ZHAO Zhipan, JIANG Xuepeng, WU Jindong, FAN Xiaopeng. Dynamic fire risk indexes for stadiums from perspective of big data [J]. China Safety Science Journal, 2022, 32(4): 155-162. |
[5] | LI Chao, DENG Xiaobao, SHI Yuntao, SUN Dehui, JIAO Yanzong. Dynamic early warning model of household gas leakage in communities [J]. China Safety Science Journal, 2022, 32(3): 90-97. |
[6] | DING Xi, ZHAO Xiaodong, WU Xinjun, ZHANG Taili, XU Zhentao. Landslide susceptibility assessment model based on multi-class SVM with RBF kernel [J]. China Safety Science Journal, 2022, 32(3): 194-200. |
[7] | LI Hongxia, XU Haoran, TIAN Shuicheng. A prediction and early warning model of miners' unsafe behavior based on random forest [J]. China Safety Science Journal, 2022, 32(12): 10-18. |
[8] | ZHANG Lidong, SONG Zeyang, LUO Zhenmin, ZHAO Shanshan. Prediction of coal spontaneous combustion period based on machine learning [J]. China Safety Science Journal, 2022, 32(12): 118-124. |
[9] | YANG Lichao, LIU Jingxian, LIU Zhao, LIU Yiwei. Association rules mining of water traffic accidents causation under value attenuation [J]. China Safety Science Journal, 2022, 32(10): 127-134. |
[10] | HAN Tianyuan, TIAN Shun, LYU Kaiguang, LI Xuan, ZHANG Jiatao, WEI Lang. Network analysis on causes for serious traffic accidents based on text mining [J]. China Safety Science Journal, 2021, 31(9): 150-156. |
[11] | ZHANG Xinsheng, CAI Baoquan. Corrosion prediction of submarine pipelines based on improved Random Forest model [J]. China Safety Science Journal, 2021, 31(8): 69-74. |
[12] | WU Renbiao, HE Yuxiang, JIA Yunfei. Key personnel risk assessment method based on improved AHP [J]. China Safety Science Journal, 2021, 31(10): 112-118. |
[13] | TIAN Rui, MENG Haidong, CHEN Shijiang, WANG Chuangye, SHI Lei. Prediction model of rockburst intensity classification based on RF-AHP-Cloud model [J]. China Safety Science Journal, 2020, 30(7): 166-172. |
[14] | HU Rui, XU Chuanling, FENG Yongtai, WEN Chao, WANG Quanquan. Prediction of different types of train delay of Guangzhou-Shenzhen high-speed railway [J]. China Safety Science Journal, 2019, 29(S2): 181-186. |
[15] | HU Liwei, HE Yueren, SHE Tianyi, MENG Ling, YANG Jinqing. Research on optimization model of expressway emergency rescue center location [J]. China Safety Science Journal, 2019, 29(5): 145-150. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||