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Method for mining illegal accident corresponding relation based on LLE and K-means method

A corresponding relationship and K-means technology, applied in the field of intelligent transportation, can solve problems such as ignoring nonlinear relationships, and achieve the effect of small randomness of initial cluster centers

Pending Publication Date: 2019-09-20
SOUTHEAST UNIV
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Problems solved by technology

In addition, in the previous studies on the corresponding relationship between accidents and violations, the linear dimensionality reduction method was mostly used, ignoring the potential nonlinear relationship between the two

Method used

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  • Method for mining illegal accident corresponding relation based on LLE and K-means method
  • Method for mining illegal accident corresponding relation based on LLE and K-means method
  • Method for mining illegal accident corresponding relation based on LLE and K-means method

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Embodiment Construction

[0058] figure 1 It is a flow chart of the method of the present invention, such as figure 1 Shown, a kind of method of the present invention is based on LLE and K-means method mining illegal accident corresponding relation, and this method comprises the following steps:

[0059] S1. Collect data required for correlation analysis between traffic violations and traffic accidents, including personnel information, traffic violation information, and traffic accident information;

[0060] S2. According to the data required for the analysis of the correlation between traffic violations and traffic accidents collected in step S1, different indicators are considered to classify traffic accidents. The indicators considered include the severity of the accident and the form of the accident;

[0061] S3. According to different classification methods of traffic accident types, select the violation type and accident type with the highest occurrence frequency as the violation label and accid...

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Abstract

The invention provides a method for mining an illegal accident corresponding relation based on an LLE and K-means method. The method comprises the following steps: collecting data required by traffic violation and traffic accident correlation analysis; classifing traffic accidents by considering different indexes; selecting an illegal type and an accident type with the highest occurrence frequency as an illegal label and an accident label of a person respectively; counting illegal types-accident types, and building an illegal types-accident type matrix; determining three thresholds to screen traffic violation types; building a personnel-type correspondence matrix; performing standardization processing on the data by using a zero-mean standardization method; reducing the data from a high dimension to a low dimension by using an LLE nonlinear dimension reduction method; carrying out clustering analysis by using an improved K-mean value algorithm for the two different accident type classification modes respectively. According to the invention, the defect of large randomness in the traditional K-means algorithm is overcome, and a corresponding relation between the traffic violation type and the traffic accident type is further mined.

Description

technical field [0001] The invention relates to a method for mining corresponding relations of illegal accidents based on LLE and K-means method, and belongs to the technical field of intelligent transportation. Background technique [0002] Road traffic accidents are affected by various factors in the traffic system, and the driving behavior of drivers is the main factor causing traffic accidents. According to previous data, more than 90% of traffic accidents are caused by human factors. In addition, among the human factors, 82% of the factors are drivers' conscious and dangerous driving behavior. The driver's dangerous driving behavior is usually recorded in the personal historical traffic violation record. Therefore, by extensively exploring historical traffic violations and traffic accident records, the intrinsic link between drivers' dangerous driving behaviors and traffic accidents can be revealed. [0003] The relationship between traffic violations and traffic acci...

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Application Information

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IPC IPC(8): G06F16/2458G06K9/62G06Q50/26
CPCG06F16/2465G06Q50/26G06F18/21355G06F18/23213Y02D10/00
Inventor 王晨宋燕超寇思元
Owner SOUTHEAST UNIV
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