Traffic trajectory clustering similarity calculation method based on shape factor adjustment

A trajectory clustering and shape factor technology, applied in computing, computer components, instruments, etc., can solve the problems of insufficient semantic understanding of traffic time and high error, and achieve good clustering effect, reasonable clustering results, and good adaptability Effect

Active Publication Date: 2019-11-12
CHANGAN UNIV
View PDF3 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a traffic trajectory clustering similarity calculation method based on shape factor adjustment, which solves the shortcomings of high error in the current urban traffic trajectory similarity algorithm. The current similarity algorithm does not fully understand the semantics of traffic time

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
  • Traffic trajectory clustering similarity calculation method based on shape factor adjustment
  • Traffic trajectory clustering similarity calculation method based on shape factor adjustment
  • Traffic trajectory clustering similarity calculation method based on shape factor adjustment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0053] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0054] see figure 1 , the present invention comprises the following steps:

[0055] Step 1: Data collection and data preprocessing

[0056] First of all, for the calculation of the algorithm and subsequent experiments, the experimental data collected is the 11,000 taxi trajectory data from February 24, 2016 to March 31, 2016 in Xi'an City as the original trajectory data set. The data of these collected original trajectory data sets cannot be directly used for calculation. Due to the existence of some abnormal points and repeated points in the original trajectory data set, it is necessary to delete or delete the abnormal data such as abnormal points and repeated points in the original trajectory data set It can only be used after preprocessing such as correction, and the data in the original trajectory dataset is processed to obtain the prep...

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 discloses a traffic trajectory clustering similarity calculation method based on shape factor adjustment, and the method comprises the steps: collecting the trajectory data of a plurality of taxies, and carrying out the preprocessing; dividing the preprocessed trajectory data set into different sets based on travel trajectory time to obtain a plurality of time period-based trajectorydata sets; calculating the trajectory data set based on the time period to obtain a point-segment distance, and optimizing the point-segment distance; calculating a segment-segment distance; finally,calculating similarity. The method is more suitable for similarity calculation of tracks in a complex urban road network, has excellent universality and has important application value in the field of track clustering.

Description

technical field [0001] The invention belongs to the field of track clustering, and relates to a traffic track clustering similarity calculation method based on shape factor adjustment. Background technique [0002] In recent years, spatio-temporal data-based analysis has become a hot topic in the field of machine learning, and trajectory clustering plays a very important role in spatio-temporal data analysis. The purpose of trajectory clustering is to aggregate trajectories of similar motion into one category. Through cluster analysis, guiding opinions on path planning can be provided, and corresponding selection support can be provided for users. In trajectory clustering, the most important work is to calculate the similarity between trajectories, so the trajectory similarity algorithm determines the effect of clustering. At the same time, the research on the similarity of traffic trajectories should not only consider the space factor but also divide it in the time area. ...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23
Inventor 康军许卫强段宗涛唐蕾樊娜杨云陈柘朱依水王青龙
Owner CHANGAN UNIV
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