Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning

Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper in...

Full description

Saved in:
Bibliographic Details
Published in:ISPRS international journal of geo-information 2017-10, Vol.6 (11), p.327
Main Authors: Luo, An, Chen, Shenghua, Xv, Bin
Format: Article
Language:English
Subjects:
Algorithms
Cell phones
Cellular telephones
Data
Data structures
Global positioning systems
GPS
hidden Markov model
map matching
Markov analysis
Markov chains
Mathematical models
mobile phone positioning
Positioning systems
Roads
route planning
Sampling
Satellite navigation systems
Sequencing
Solutions
Speech
Speech recognition
Statistical models
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM) for GPS positioning and mobile phone positioning with a low sampling rate. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. The sequence consists of hidden states in the HMM model. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi6110327