Bidirectional detection and restoration method for traffic flow abnormal data based on KNN algorithm

A KNN algorithm and abnormal data technology, applied in the field of intelligent transportation systems, can solve problems such as low repair accuracy and reduced repair accuracy, and achieve the effect of improving traffic data quality, repair accuracy, and quality.

Active Publication Date: 2018-05-01
SHANGHAI UNIV OF ENG SCI
View PDF3 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The historical average method and the moving average method mainly use data to calculate the average value, and the repair accuracy is not high; the interpolation method is mainly used

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Bidirectional detection and restoration method for traffic flow abnormal data based on KNN algorithm
  • Bidirectional detection and restoration method for traffic flow abnormal data based on KNN algorithm
  • Bidirectional detection and restoration method for traffic flow abnormal data based on KNN algorithm

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0065] Examples:

[0066] Taking a set of data as an example, the specific implementation steps of the scheme of the present invention will be further described in detail.

[0067] A. Select the normal traffic flow speed data for any 5 days of a certain expressway in February as the historical data. According to the time series, select 5 consecutive normal data as a group to establish a historical data vector database X n , X n ={v h1 , V h2 , V h3 , V h4 , V h5 };

[0068] B. Select the abnormal speed data of a certain day in February as the data to be repaired;

[0069] C. Identify an abnormal value in the speed data to be repaired and mark it as v(w), as attached figure 2 , Then v(w)=v 4 ;

[0070] D. Establish abnormal data state vector X, X={v 1 , V 2 , V 3 , V 4 , V 5 }, at this time v 4 For abnormal values, the specific steps for establishing abnormal data state vector X are:

[0071] Starting from the two directions before and after the location of the abnormal data v(w), first ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a bidirectional detection and restoration method for traffic flow abnormal data based on a KNN algorithm, and the method comprises the following steps: 1), obtaining normal traffic flow historical data, taking each five pieces of continuous data as a group, and building a historical data state vector library; 2), obtaining an abnormal value in a to-be-restored traffic flowdata, and marking the abnormal value as v(w); 3), constructing an abnormal data state vector X according to the abnormal value; 4), calculating the Euclidean distances d between the abnormal data state vector X and all historical data state vectors Xn, carrying out the optimization, and obtaining k groups of historical data state vectors after optimization and the corresponding Euclidean distances di (i=1, 2, ...k) 5), calculating a restoration value v(w)' according to the k groups of historical data state vectors after optimization and the corresponding Euclidean distances di; 6), carrying out the deletion, filling and restoration of the abnormal value. Compared with the prior art, the method is high in restoration precision, is high in applicability, improves the restoration precision,and improves the traffic data quality.

Description

technical field [0001] The invention relates to the field of intelligent traffic systems, in particular to a method for bidirectional detection and repair of abnormal traffic flow data based on KNN algorithm. Background technique [0002] Complete traffic flow data is the basis of traffic management and control. The vehicle detector is a device that detects road traffic flow operating parameters and is an important component of the intelligent transportation system. In actual traffic operation, due to abnormal road traffic environment, the detector Failure, communication failure and other reasons lead to abnormalities in the collected traffic data, which affects the quality of traffic data. The quality of traffic flow data directly affects the effect of estimation, prediction and evaluation of traffic status. Therefore, it is necessary to restore traffic anomaly data. [0003] The current restoration methods for abnormal traffic flow data mainly include historical average m...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G08G1/01G08G1/065
CPCG08G1/0129G08G1/0133G08G1/065
Inventor 秦一菲马明辉王岩松张亮郭辉刘宁宁王孝兰
Owner SHANGHAI UNIV OF ENG SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap