Driving behavior analysis method based on improved K-means

A behavioral analysis and behavioral technology, applied in the direction of instruments, character and pattern recognition, data processing applications, etc., can solve the problems of scientificity and poor accuracy of driving behavior clustering results, achieve high practical application value, and improve accuracy And the effect of high stability and accuracy

Inactive Publication Date: 2020-07-28
HARBIN ENG UNIV
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Problems solved by technology

[0024] The purpose of the present invention is to solve the problem that the current optimized K-means method is applied to a specific driving scene, and the existing problems of

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  • Driving behavior analysis method based on improved K-means
  • Driving behavior analysis method based on improved K-means
  • Driving behavior analysis method based on improved K-means

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Experimental program
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specific Embodiment approach 1

[0057] Specific implementation mode one: as figure 1 and Figure 4 As shown, a kind of driving behavior analysis method based on improved K-means described in this embodiment, the method comprises the following steps:

[0058] Step 1. Collect the raw data of driving behavior of m drivers, preprocess the collected raw data, and obtain the preprocessed data;

[0059] Step 2, extracting several characteristic parameter values ​​from the preprocessed data, and then standardizing the extracted characteristic parameter values ​​to obtain standardized characteristic parameter values;

[0060] Step 3, extracting the speeding tendency behavior factor P1 and the variable speed driving behavior factor P2 according to the characteristic parameter values ​​after standardization, and calculating the score coefficient of each characteristic parameter value on the factor P1 and the score coefficient of each characteristic parameter value on the factor P2;

[0061] According to the score coe...

specific Embodiment approach 2

[0088] Embodiment 2: The difference between this embodiment and Embodiment 1 is that in the first step, the collected raw data is preprocessed, and the preprocessing method includes: filling missing values, filtering abnormal data, and deleting parking data.

[0089] During the driving process of the vehicle, the on-board sensors may be interfered by other devices, and there are abnormal values ​​in the collected driving data. Therefore, the collected data needs to be processed in advance before analysis, that is, data preprocessing.

[0090] (1) Filling of missing values

[0091] Data may be disturbed by various factors during the process of collection, transmission, and storage, resulting in data loss and incompleteness, resulting in data loss in Redis and MySQL databases. Missing values ​​are often handled by ignoring or imputing. When there are multiple missing values ​​consecutively in the data set, the method of ignoring the missing values ​​is adopted, that is, these m...

specific Embodiment approach 3

[0099] Specific embodiment three: the difference between this embodiment and specific embodiment one is that in the step two, several characteristic parameter values ​​are extracted from the preprocessed data, and the characteristic parameter values ​​include the vehicle speed average v a , speed standard deviation v s , overspeed time ratio η, daily average overspeed times, acceleration standard deviation a s , the daily average number of sudden accelerations and the daily average number of sudden brakes.

[0100] (1) Average speed v a

[0101] The higher the average vehicle speed, the greater the probability of a traffic accident.

[0102]

[0103] In the formula: v m is the vehicle speed value collected for the mth time; n is the total number of collected vehicle speed samples; v a is the average speed of the bus.

[0104] (2) Speed ​​standard deviation v s

[0105] The larger the standard deviation of vehicle speed, the greater the dispersion of vehicle speed di...

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Abstract

The invention discloses a driving behavior analysis method based on improved K-means, and belongs to the technical field of driving behavior analysis. According to the method, the problem that the existing K-means method is poor in scientificity and accuracy of a driving behavior clustering result is solved. In order to select an optimal initial center, the invention provides a DC algorithm, the algorithm calculates the product of the density of samples in a data set, the reciprocal of the average difference degree among the samples in the class and the difference degree among the clusters asa center index, and the initial center is determined by the center index; then the initial center obtained through the DC algorithm is used as a default parameter to be input into the K-means algorithm, so that the accuracy and stability of the K-means algorithm on the clustering result of the driving behaviors are improved, and the clustering result of the improved K-means algorithm on the driving behaviors is more scientific. Experimental results show that on the research of driving behavior clustering analysis, the improved algorithm is higher in accuracy and stronger in anti-interference capability, and the accuracy reaches 90%. The method can be applied to driving behavior analysis.

Description

technical field [0001] The invention belongs to the technical field of driving behavior analysis, in particular to a driving behavior analysis method based on improved K-means. Background technique [0002] At present, researchers' research on driving behavior mainly starts from two perspectives: driving behavior evaluation and driving behavior analysis. Fair and reasonable evaluation of drivers and accurate identification of the purpose of driver's driving behavior provide powerful indicators and scientific basis for the excavation of objective factors that have an important impact on driving behavior, and then provide a powerful indicator and scientific basis for transportation companies to evaluate drivers. [0003] Based on the method of data mining, Zheng Hengjie uses the Isolation Forest algorithm and the SOM algorithm to preprocess the traffic data and extract the feature values, and then constructs a classifier through the K-means clustering algorithm and BP neural n...

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Application Information

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IPC IPC(8): G06K9/62G06Q10/06
CPCG06Q10/06393G06F18/23213
Inventor 吴艳霞李储岩王旭王青文
Owner HARBIN ENG UNIV
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