Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Road network trajectory clustering analysis method based on improved DPC algorithm

A road network and trajectory clustering technology, which is applied in the field of cluster analysis, can solve the problems that the classic trajectory cannot represent the whole situation, and there are too many parameter settings, so as to reduce the influence of custom parameters, improve adaptability, and improve accuracy.

Active Publication Date: 2020-04-21
UNIV OF ELECTRONICS SCI & TECH OF CHINA
View PDF3 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing road trajectory clustering cannot describe the hotspots well, and the obtained classic trajectory often cannot represent the overall s

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
  • Road network trajectory clustering analysis method based on improved DPC algorithm
  • Road network trajectory clustering analysis method based on improved DPC algorithm
  • Road network trajectory clustering analysis method based on improved DPC algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0074] A kind of road network locus cluster analysis method based on improved DPC algorithm that the preferred embodiment of the present invention provides, comprises the following steps of carrying out successively:

[0075] A road network track clustering analysis method based on the improved DPC algorithm, comprising the following steps carried out in sequence:

[0076] S1: Data acquisition: use vehicle-mounted and ground-based road track recording equipment to collect track data of moving objects, or collect GPS data of different moving objects, and use track data or GPS data as input data;

[0077] S2: Trajectory movement expression: obtain the sub-trajectory sequence connected by feature points from the input data of step S1;

[0078] Extract valid global trajectory data from the input data in step S1; build a model through linear interpolation and semantic extension based on the local interpolation model, output the trajectory file, and select a class of anomalous point...

Embodiment 2

[0083] In this embodiment, on the basis of Embodiment 1, the expression of the trajectory movement in step S2 has the following steps:

[0084] S21: Establish a trajectory model based on the line segment trajectory representation, extract the stay point from the input data in step S1 for semantic expansion, and convert the stay point trajectory into a position trajectory;

[0085] S22: Road trajectory data expression based on local interpolation model: After discretizing the road trajectory grid, use the inverse distance weight method to calculate the attribute values ​​of the trajectory segments falling in each grid, and calculate the trajectory segment according to the attributes of the adjacent sampling points perform interpolation;

[0086] S23: Find the feature points based on the separation method of the trajectory segment of the angle size, and obtain the sub-trajectory sequence connected by the feature points.

Embodiment 3

[0088] In this embodiment, on the basis of Embodiment 1, the aggregation distance CD in the step S3 to calculate the distance between sub-trajectories has the following steps:

[0089] S31: For the sub-trajectory segments formed after any two trajectories are expressed, calculate the aggregation distance CD between the sub-trajectory segments, assuming two sub-trajectory segments ST i =b i e i and ST j =b j e j , using d ⊥ (ST i ,ST j ) represents the vertical distance between two sub-trajectories, using d || (ST i ,ST j ) represents the parallel distance between two sub-trajectories, using d θ (ST i ,ST j ) represents the angular distance between two sub-trajectories;

[0090] S32: Sub-track ST i and ST j The track distance CD(ST i ,ST j ), that is, the similarity between trajectories, expressed as the weighted average of three normalized distances;

[0091] S33: Calculate the aggregation distance CD between any two sub-trajectories, and finally obtain the s...

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 road network trajectory clustering analysis method based on an improved DPC algorithm, and belongs to the field of clustering analysis methods, and the method comprises the steps: S1, carrying out the effective collection of a road moving object trajectory; s2, expressing trajectory data; s3, calculating the distance between the sub-trajectories by adopting an aggregationdistance CD, fusing the vertical distance, the parallel distance and the angle distance by the distance, and obtaining inter-trajectory similarity measurement on the basis of the CD distance; s4, according to the fused CD distance matrix, carrying out sub-track clustering by utilizing an improved DPC algorithm; and S5, for the obtained clustering result, extracting a classical trajectory of the clustering result as a new behavior mode of the whole road network. According to the method, the precision of road trajectory measurement is improved, compared with a traditional density clustering algorithm, the influence of self-defined parameters is reduced, the interactivity with a user is improved, and it is beneficial to discover meaningful trajectory modes in a road network.

Description

technical field [0001] The invention belongs to the field of cluster analysis, and relates to a road network track cluster analysis method based on an improved DPC algorithm. Background technique [0002] The full name of the clustering algorithm based on density peaks is the clustering algorithm based on fast search and find of density peaks (English name: clustering by fast search and find of density peaks, abbreviated as DPC). It is a clustering algorithm proposed in Science in 2014, which can automatically find cluster centers and realize efficient clustering of arbitrary shape data. [0003] With the rapid development of technologies such as smart terminals, mobile positioning, and wireless communications, a large amount of trajectory data constrained by road networks has been collected in application fields such as transportation and logistics. Using trajectory data to analyze paths can reflect the movement and behavior patterns of moving objects. However, the existi...

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): G06K9/62G06F16/29
CPCG06F16/29G06F18/2321
Inventor 牛新征刘鹏飞郑云红望馨刘翔宇
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com