Open AccessIdentifying Expressway Accident Black Spots Based on the Secondary Division of Road Units
Author(s) -
Guanfeng Liang,
Xujiao Sun,
Yidan Zhang,
Mingli Chen,
Wanting Zhang
Publication year - 2021
Publication title -
promet
Language(s) - English
Resource type - Journals
eISSN - 1848-4069
pISSN - 0353-5320
DOI - 10.7307/ptt.v33i5.3680
Subject(s) - black spot , bayes' theorem , identification (biology) , transport engineering , computer science , accident (philosophy) , cluster analysis , engineering , data mining , bayesian probability , artificial intelligence , philosophy , botany , epistemology , horticulture , biology
For the purpose of reducing the harm of expressway traffic accidents and improving the accuracy of traffic accident black spots identification, this paper proposes a method for black spots identification of expressway accidents based on road unit secondary division and empirical Bayes method. Based on the modelling ideas of expressway accident prediction models in HSM (Highway Safety Manual), an expressway accident prediction model is established as a prior distribution and combined with empirical Bayes method safety estimation to obtain a Bayes posterior estimate. The posterior estimated value is substituted into the quality control method to obtain the black spots identification threshold. Finally, combining the Xi'an-Baoji expressway related data and using the method proposed in this paper, a case study of Xibao Expressway is carried out, and sections 9, 19, and 25 of Xibao Expressway are identified as black spots. The results show that the method of secondary segmentation based on dynamic clustering can objectively describe the concentration and dispersion of accident spots on the expressway, and the proposed black point recognition method based on empirical Bayes method can accurately identify accident black spots. The research results of this paper can provide a basis for decision-making of expressway management departments, take targeted safety improvement measures.