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

1, 2,* and 2
Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China
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;
Received: 13 August 2017 / Revised: 28 September 2017 / Accepted: 24 October 2017 / Published: 30 October 2017
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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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