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Open AccessArticle

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

1
Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China
2
Guangzhou Aochine Robot Technology Ltd., Room 3A04, Sicheng Road, Tianhe District, Guangzhou 510663, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(11), 327; https://doi.org/10.3390/ijgi6110327
Received: 13 August 2017 / Revised: 28 September 2017 / Accepted: 24 October 2017 / Published: 30 October 2017
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. View Full-Text
Keywords: map matching; hidden Markov model; mobile phone positioning; route planning map matching; hidden Markov model; mobile phone positioning; route planning
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Luo, A.; Chen, S.; Xv, B. Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning. ISPRS Int. J. Geo-Inf. 2017, 6, 327.

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