Mediating Effect Analysis on Traffic Incident Discovery Time between Influence Factors and Duration Time on Expressways

被引:0
作者
Chen, Jiao-Na [1 ]
Tao, Wei-Jun [1 ]
Zhang, Xiang [2 ]
Ma, Li [3 ]
机构
[1] Xian Shiyou Univ, Sch Elect Engn, Xian, Peoples R China
[2] CCC First Highway Consultants Co Ltd, Xian, Peoples R China
[3] Broadvis Engn Consultants Co Ltd, Kunming, Yunnan, Peoples R China
来源
CICTP 2023: INNOVATION-EMPOWERED TECHNOLOGY FOR SUSTAINABLE, INTELLIGENT, DECARBONIZED, AND CONNECTED TRANSPORTATION | 2023年
关键词
traffic accident; discovery time; Duration time; mediating effect;
D O I
暂无
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
To analyze and validate the mechanism between discovery time and duration time in traffic incidents, 23,268 samples from the Shaan'xi Province in China were collected by the expressway monitoring and control system. First, discovery time and influencing factors were introduced to construct the theoretical model, and then the hypotheses among the variables and intermediary effects were verified based on SPSS. The results show that discovery time and duration time are positively correlated. Discovery time not only directly affects duration time but also can indirectly by three mediating effect paths. Consequently, the discovery time is statistically significant and positively mediates and reduces the duration of expressway traffic incidents. Therefore, the important reference value of the research results lies in that discovery time is the first phase in the entire duration time. Therefore, it is the key factor affecting duration time to improve response efficiency.
引用
收藏
页码:1194 / 1203
页数:10
相关论文
共 9 条
[1]   Exploring the influential factors in incident clearance time: Disentangling causation from self-selection bias [J].
Ding, Chuan ;
Ma, Xiaolei ;
Wang, Yinhai ;
Wang, Yunpeng .
ACCIDENT ANALYSIS AND PREVENTION, 2015, 85 :58-65
[2]   Modelling transport time to trauma centres and 30-day mortality in road accidents in Switzerland: an exploratory study [J].
Diserens, Raphael Victor ;
Marmy, Clotilde ;
Pasquier, Mathieu ;
Zingg, Tobias ;
Joost, Stephane ;
Hugli, Olivier .
SWISS MEDICAL WEEKLY, 2021, 151
[3]   Factors affecting injury severity and the number of vehicles involved in a freeway traffic accident: investigating their heterogeneous effects by facility type using a latent class approach [J].
Jeon, Hyeonmyeong ;
Kim, Jinhee ;
Moon, Yeseul ;
Park, Juneyoung .
INTERNATIONAL JOURNAL OF INJURY CONTROL AND SAFETY PROMOTION, 2021, 28 (04) :521-530
[4]   Sequential Prediction for Large-Scale Traffic Incident Duration: Application and Comparison of Survival Models [J].
Li, Xiaobing ;
Liu, Jun ;
Khattak, Asad ;
Nambisan, Shashi .
TRANSPORTATION RESEARCH RECORD, 2020, 2674 (01) :79-93
[5]   An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors [J].
Ma, Zhengjing ;
Mei, Gang ;
Cuomo, Salvatore .
ACCIDENT ANALYSIS AND PREVENTION, 2021, 160 (160)
[6]   Estimation of Traffic Incident Duration: A Comparative Study of Decision Tree Models [J].
Saracoglu, Abdulsamet ;
Ozen, Halit .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2020, 45 (10) :8099-8110
[7]   A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis [J].
Shang, Qiang ;
Tan, Derong ;
Gao, Song ;
Feng, Linlin .
JOURNAL OF ADVANCED TRANSPORTATION, 2019, 2019
[8]  
Zainab A. M., 2020, Journal of Research in Science and Engineering, V2, P20
[9]   Do Larger Sample Sizes Increase the Reliability of Traffic Incident Duration Models? A Case Study of East Tennessee Incidents [J].
Zhang, Zihe ;
Liu, Jun ;
Li, Xiaobing ;
Khattak, Asad J. .
TRANSPORTATION RESEARCH RECORD, 2021, 2675 (06) :265-280