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

Segmentation-based hidden Markov model map matching method

A hidden Markov and model map technology, applied in road network navigators and other directions, can solve the problems of error sensitivity and low efficiency

Active Publication Date: 2019-08-06
NORTHWEST UNIV
View PDF4 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of the above problems, the object of the present invention is to provide a segmentation-based hidden Markov model map matching method, which solves the problem that the point-based map matching method is inefficient and sensitive to errors

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
  • Segmentation-based hidden Markov model map matching method
  • Segmentation-based hidden Markov model map matching method
  • Segmentation-based hidden Markov model map matching method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0051] This embodiment provides a segmentation-based hidden Markov model map matching method, the overall framework is as follows figure 1 , is mainly divided into three layers: data preprocessing layer, trajectory segmentation and candidate path search layer, hidden Markov model matching layer, the specific implementation steps of this method are as follows:

[0052] Step 1, data preprocessing:

[0053] The two methods of removing noise data used in this method are both conventional methods, and only a brief description is given: In order to avoid the impact of noise in the trajectory data on the final matching performance, the original trajectory data is preprocessed, and the noise data is processed. remove. First, assume that if a GPS point is far from any road segment, it is less likely to match the road network. Given a distance threshold r(40m), if there is no road segment within the radius r of a GPS point, the GPS point is considered noise. like figure 2 as shown,...

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 segmentation-based hidden Markov model map matching method. The method comprises the following steps: step one, carrying out noise processing on a GPS trajectory and establishing a R-tree spatial index on a road network; step two, carrying out segmentation on the GPS trajectory based on an angle and searching a candidate path set corresponding to the segmented sub-trajectory segments; and step three, selecting a path with the highest probability corresponding to the trajectory as a matching result by using a hidden Markov model. Therefore, a problem of low efficiencyof the GPS trajectory point-by-point map matching method is solved; and the accuracy of map matching is improved.

Description

technical field [0001] The invention belongs to the technical field of data mining, and relates to a segmentation-based map matching method. Background technique [0002] A GPS track is a sequence of GPS records that can effectively record the spatial trajectory of a moving object. With the popularity of mobile devices, a large amount of GPS trajectory data is widely used in various fields. Due to measurement errors and low sampling rates of GPS receivers, there is often uncertainty in the spatial location of GPS trajectories, so it is necessary to match GPS trajectories to road networks during a preprocessing step for many applications, such as urban mobility computing, Route navigation, transportation analysis and management, etc. Therefore, efficient and accurate map matching is urgently needed. [0003] Nowadays, map matching is an active research area. Most current map matching methods are point-based matching methods, which process GPS points separately in the map ...

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): G01C21/32
CPCG01C21/32
Inventor 王欣崔革边文涛
Owner NORTHWEST UNIV
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