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Traffic violation severity level prediction method based on Bayesian network

A technology of Bayesian network, forecasting method, applied in the field of traffic psychology, to achieve the effect of good severity level

Inactive Publication Date: 2018-01-30
SUN YAT SEN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, as one of the main causes of traffic accidents, traffic violations are rarely studied

Method used

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  • Traffic violation severity level prediction method based on Bayesian network
  • Traffic violation severity level prediction method based on Bayesian network
  • Traffic violation severity level prediction method based on Bayesian network

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Experimental program
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Embodiment 1

[0036] A method for predicting the severity level of traffic violations based on Bayesian networks, comprising the following steps:

[0037] S1: Determine the variables of the Bayesian model;

[0038] S2: Establish a Bayesian model and train the model.

[0039] Further, the specific process of the step S1 is:

[0040] According to the analysis of the driver's gender, age, driving experience, time period of illegal incidents, types of illegal vehicles, vehicle attribution and severity of traffic violations, these factors are determined as variables of the Bayesian model, a training set and parameters in the training set can be expressed as figure 1 Shown:

[0041] X={G,A,D,T,W,V,O,L} (1)

[0042] in:

[0043] G={0,1} means gender, 0 means female, 1 means male;

[0044] A={0,1,2,3,4,5} means age, where 0 means 18-20 years old, 1 means 21-30 years old, 2 means 31-40 years old, 3 means 41-50 years old, 4 means 51 years old -60 years old, 5 means 61 years old and above;

[...

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Abstract

The invention provides a traffic violation severity level prediction method based on a Bayesian network. According to the method, directed diagram description based on the network structure is performed, the association relationship and the degree of influence between all the elements are described by using a directed acyclic graph in the network structure, all the elements are expressed by node variables, the association relationship between all the elements is expressed by the directed edges between the nodes, and the degree of influence between all the elements is described by the conditional probability. According to the method, the severity level of the traffic violation can be better predicted by the established Bayesian network model, and the prediction result can be applied to traffic violation management and traffic accident prevention.

Description

technical field [0001] The invention relates to the field of traffic psychology, and more specifically, to a method for predicting the severity level of traffic violations based on a Bayesian network. Background technique [0002] In recent years, my country's car ownership has grown rapidly, reaching 279 million in 2015. At the same time, violations of laws and regulations by motor vehicle drivers are common. According to statistics, a total of 442 million traffic violations were investigated and dealt with nationwide in 2015, and nearly 90% of traffic accidents with personal injuries were caused by illegal acts. [0003] Scholars at home and abroad have conducted some research on traffic accidents: Ma Zhuanglin and others used the cumulative Logistic regression model to study the factors affecting the severity of traffic accidents; Li Juan and others studied the traffic accidents based on the improved BP neural network model. The method of predicting the number of times, ...

Claims

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Application Information

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IPC IPC(8): G08G1/01G06Q10/04G06Q50/30G06N7/00
Inventor 邓院昌刘祺金杰灵
Owner SUN YAT SEN UNIV
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