Study on the Influence Factors on the Severity of Heavy Goods Vehicle Traffic Accidents

被引:0
作者
Lu, Feng [1 ]
Yang, Shuo [2 ]
机构
[1] Peoples Publ Secur Univ China, Beijing, Peoples R China
[2] Minist Sci & Technol Peoples Republ China, Ctr Logist, Beijing, Peoples R China
来源
CICTP 2022: INTELLIGENT, GREEN, AND CONNECTED TRANSPORTATION | 2022年
关键词
INJURY SEVERITY;
D O I
暂无
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
With the advantage of large volume capacity, heavy goods vehicles have become an important part of road freight transport. However, the proportion of heavy goods vehicles involved in traffic accidents is very high, especially in traffic accidents with serious casualties. These traffic accidents have brought great harm to lives and property, so it is necessary to study the factors influencing the traffic accident severity of heavy goods vehicles and corresponding countermeasures. Based on traffic accident data of heavy goods vehicles in Shenzhen, the statistical analysis of heavy goods vehicle traffic accidents had been examined from the aspects of driver, vehicle, road, and environment. The characteristics of heavy goods vehicle traffic accidents have also been identified, and the influence factors of heavy goods vehicle traffic accident severity have been singled out. With the number of fatal accidents in traffic accident data as a reference, the main factors strongly related to the heavy goods vehicle traffic accident severity were determined through Grey correlation analysis. We then built the data set and the test set based on the selected main factors. We established a Bayesian network model to analyze the relationship between the heavy goods vehicle traffic accident severity and influence factors. The structure of Bayesian network was constructed. The parameter estimation of Bayesian network was conducted by EM algorithm, and the validity of the model has been verified. Then the reasoning analysis was carried out by applying this model. Based on the results of Bayesian network reasoning analysis, both the hidden dangers of heavy goods vehicle traffic accident and problems in the supervision and management of heavy goods vehicles were analyzed, and we proposed countermeasures of strengthening supervision and management and improving road hardware conditions to improve the traffic safety level of heavy goods vehicles.
引用
收藏
页码:1997 / 2006
页数:10
相关论文
共 18 条
  • [1] Severity analysis for large truck rollover crashes using a random parameter ordered logit model
    Azimi, Ghazaleh
    Rahimi, Alireza
    Asgari, Hamidreza
    Jin, Xia
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2020, 135
  • [2] The use of the area under the roc curve in the evaluation of machine learning algorithms
    Bradley, AP
    [J]. PATTERN RECOGNITION, 1997, 30 (07) : 1145 - 1159
  • [3] Analysis of traffic injury severity: An application of non-parametric classification tree techniques
    Chang, Li-Yen
    Wang, Hsiu-Wen
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2006, 38 (05) : 1019 - 1027
  • [4] Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
    Chen, Huan-Huan
    Tang, Rui
    Zhang, Hao-Ran
    Yu, Yi
    Wang, Yuntao
    [J]. JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, 2020, (160): : 1 - 22
  • [5] Chen Yanyan, 2018, TRANSPORTATION TECHN, V20, P1
  • [6] Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks
    Delen, D
    Sharda, R
    Bessonov, M
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2006, 38 (03) : 434 - 444
  • [7] Deng J.L., 1990, Grey system theory
  • [8] Haq M. T., 2020, ACCIDENT ANAL PREVEN, V144
  • [9] Jian X. Y., 2019, FDN APPL BAYESIAN NE
  • [10] Analyzing truck accident data on the interurban road Ankara-Aksaray-Eregli in Turkey: Comparing the performances of negative binomial regression and the artificial neural networks models
    Kibar, Funda Ture
    Celik, Fazil
    Wegman, Fred
    [J]. JOURNAL OF TRANSPORTATION SAFETY & SECURITY, 2019, 11 (02) : 129 - 149