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Bayesian network model based risk evaluation method for road transportation accident

A technology of Bayesian network and model estimation, applied in special data processing applications, instruments, electrical digital data processing, etc., which can solve problems such as failing to consider the superposition of parallel factors and offsetting the impact of final evaluation results

Inactive Publication Date: 2015-11-11
BEIJING NORMAL UNIVERSITY
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Problems solved by technology

[0004] The present invention aims to solve the above-mentioned problems, that is, to solve the problem that the existing methods fail to consider the influence of the superposition and offset between the juxtaposed factors on the final evaluation result

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  • Bayesian network model based risk evaluation method for road transportation accident
  • Bayesian network model based risk evaluation method for road transportation accident
  • Bayesian network model based risk evaluation method for road transportation accident

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Embodiment Construction

[0016] The technical solutions of the present invention are described below in combination with preferred embodiments. see first figure 1 , which is a schematic diagram of a Bayesian network model according to the present invention. Such as figure 1 As shown, the Bayesian network model of the present invention is constructed into a three-layer network structure, and the three-layer network structure includes: root nodes B, C, D, E, F, G; intermediate nodes C1, C2, C3; and evaluation Object A. As an example, root nodes B, C, D, E, F, and G represent vehicle type, driver age, driver gender, weather conditions, visibility, and vehicle condition; intermediate nodes C1, C2, and C3 represent human factors, road and environmental factors and vehicle factors; evaluation object A represents road traffic accidents on highway bridges. It should be noted that the content and quantity of the root node and intermediate nodes here are only given as examples, and those skilled in the art ...

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Abstract

The present invention relates to accident probability evaluation, particularly provides a Bayesian network model based risk evaluation method for a road transportation accident, and aims to solve the problem that the influence of superposition and counteraction among root node factors on a final evaluation result is not considered in an existing method. For the purpose, the method comprises: acquiring factors related to the road transportation accident; constructing a Bayesian network model of the road transportation accident according to the factors; and estimating a risk probability of the road transportation accident according to the Bayesian network model. The method is characterized in that the Bayesian network model has a three-layer network structure, and the method evaluates the probability of an intermediate node according to the probability of a root node and evaluates the risk probability of the road transportation accident according to the probability of the intermediate node. According to the method, superposition and / or counteraction effects among the factors of the root node are considered when the Bayesian network model is constructed, so that the accuracy of final evaluation can be greatly improved.

Description

technical field [0001] The invention relates to accident probability assessment, and specifically provides a risk assessment method for road transportation accidents based on a Bayesian network model. Background technique [0002] The purpose of risk analysis is to provide decision support to users or decision-making platforms. There are many reasons that may lead to accidents. The probability of accidents and the complexity and uncertainty of accident causes make risk prediction extremely uncertain. The main research methods for accident risk include fault tree, Markov chain, dynamic fault tree, Petrinets, Bayesian network. Bayesian network has strong accident reasoning and accident cause diagnosis functions, and is one of the common methods of risk analysis. It cannot directly judge the occurrence of accidents, but it can identify the relationship between various state factors in complex systems, and can Find the strongest correlation among the many possible causes of an...

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Application Information

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IPC IPC(8): G06F17/50
Inventor 易雨君杨志峰唐彩红
Owner BEIJING NORMAL UNIVERSITY
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