Abstract
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.
Highlights
According to the data from the China Statistical Yearbook [1], there were 203,000 traffic accidents in China in 2017, resulting in more than 60,000 deaths and more than 200,000 injuries, resulting in direct economic losses of about 1.2 billion yuan
In order to overcome the shortcomings of the fixed length division method in accident black spot recognition, this paper introduces the road linear indicators and the accumulation of traffic accidents on the road into the road unit division
A series of research are conducted on the rational division of highway units, the establishment of accident prediction models, and the combination of empirical Bayes method and quality control method to identify black spots on highway accidents
Summary
According to the data from the China Statistical Yearbook [1], there were 203,000 traffic accidents in China in 2017, resulting in more than 60,000 deaths and more than 200,000 injuries, resulting in direct economic losses of about 1.2 billion yuan. Many scholars have studied the relationship between road alignment, road facilities, location speed, number of traffic conflicts, and traffic accident rate, and improved the accident black spot identification method [18,19,20]. Scholars have noticed that the length and starting and ending positions of accident statistical sections will directly affect the calculation results of accident rate and the final determination of accident black spots [4]. The rationality of road unit division is rarely considered, and the accident distribution and road section division are not well combined; on the other hand, the regression effect of statistical accident number is not considered Taking the two-way eight-lane expressway from Xi'an to Baoji as an example, this paper uses the method proposed in this paper to divide the road units, establish and demarcate the accident prediction model, and identify the accident black spots
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