Abstract
The purpose of this study is to minimize the negative influences of the severe traffic accidents in China by profoundly analyzing the complex coupling relations among accident factors contributing to the single-vehicle and multivehicle traffic accidents with the Bayesian network (BN) crash severity model. The BN model was established by taking the critical factors identified with the improved grey correlation analysis method as node variables. The severe traffic accident data collected from accident reports published in China were used to validate this model. The model’s efficiency was validated objectively by comparing the conditional probability obtained by this model with the actual value. The result shows that the BN model can reflect the real relations among factors and can be seen as the target network for the severe traffic accidents in China. Besides, based on BN’s junction tree engine, five-factor combination sequences for the number of deaths and three-factor combination sequences for the number of injuries were ranked according to the severity degree to reveal the critical reasons and reduce the massive traffic accidents damage.
Highlights
Severe traffic accidents occur in random form regardless of time and space [1]
The enormous negative impacts of severe traffic accidents on public opinion and personal property security need to be noticed by the traffic administration and scholars in the field of traffic safety [3,4,5]
Few indepth discussions have been conducted on the mechanism by the objective data [40, 41]. is paper aims to identify the critical factors contributing to severe single-vehicle and multivehicle traffic accidents separately and explore the inherent relationships among different factors based on objective data. rough a comprehensive comparison of these factors, some recommendations can be made in this paper for active precaution system construction
Summary
Severe traffic accidents occur in random form regardless of time and space [1]. Mass casualties and high risk are two main distinctive features that can quickly differentiate severe traffic accidents from general accidents [2]. Drivers’ behaviors are considered as the main factors causing traffic accidents in early studies. Some scholars believe that drivers’ illegal behaviors significantly impact road traffic safety [13], and drivers themselves are related to accidents [14,15,16]. When establishing the Bayesian network model, the researchers have comprehensively considered the decision variables of solving the problem and the relationship among various factors. Is paper aims to identify the critical factors contributing to severe single-vehicle and multivehicle traffic accidents separately and explore the inherent relationships among different factors based on objective data. An improved grey correlation analysis method and BN traffic severity model were constructed in this paper. The weighted grey relational degree was used to determine the critical factors contributing to single-vehicle and multivehicle traffic accidents, respectively. The conditional probability based on Bayesian estimation was used to validate the model’s efficiency
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