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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-10-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/6/11/327 |
| _version_ | 1818041667464200192 |
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| author | An Luo Shenghua Chen Bin Xv |
| author_facet | An Luo Shenghua Chen Bin Xv |
| author_sort | An Luo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T08:34:04Z |
| format | Article |
| id | doaj.art-bf150719609444c797f22d92af8b32d2 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-10T08:34:04Z |
| publishDate | 2017-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-bf150719609444c797f22d92af8b32d22022-12-22T01:56:02ZengMDPI AGISPRS International Journal of Geo-Information2220-99642017-10-0161132710.3390/ijgi6110327ijgi6110327Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone PositioningAn Luo0Shenghua Chen1Bin Xv2Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, ChinaGuangzhou Aochine Robot Technology Ltd., Room 3A04, Sicheng Road, Tianhe District, Guangzhou 510663, ChinaGuangzhou Aochine Robot Technology Ltd., Room 3A04, Sicheng Road, Tianhe District, Guangzhou 510663, ChinaNumerous 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.https://www.mdpi.com/2220-9964/6/11/327map matchinghidden Markov modelmobile phone positioningroute planning |
| spellingShingle | An Luo Shenghua Chen Bin Xv Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning ISPRS International Journal of Geo-Information map matching hidden Markov model mobile phone positioning route planning |
| title | Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning |
| title_full | Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning |
| title_fullStr | Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning |
| title_full_unstemmed | Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning |
| title_short | Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning |
| title_sort | enhanced map matching algorithm with a hidden markov model for mobile phone positioning |
| topic | map matching hidden Markov model mobile phone positioning route planning |
| url | https://www.mdpi.com/2220-9964/6/11/327 |
| work_keys_str_mv | AT anluo enhancedmapmatchingalgorithmwithahiddenmarkovmodelformobilephonepositioning AT shenghuachen enhancedmapmatchingalgorithmwithahiddenmarkovmodelformobilephonepositioning AT binxv enhancedmapmatchingalgorithmwithahiddenmarkovmodelformobilephonepositioning |