Abstract
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.
Highlights
Map-matching is a basic operation for improving positioning accuracy by integrating positioning data with spatial road network data to identify the correct road link on which a vehicle is travelling and to determine the location of a vehicle on a road link
A hidden Markov model (HMM)-based map-matching algorithm can be employed as a key component to improve the performance of systems that support the navigation function of intelligent transport systems (ITSs)
Map-matching based on probabilistic strategy, including HMM-based map-matching for Global Positioning System (GPS) positioning [23,24,25,26], and multiple hypothesis [27,28,29,30] focuses on the perspective of the total situation for all position data and all candidate road links [7,15,16,31,32,33,34], instead of calculation between individual positions and nearby candidate road links
Summary
Map-matching is a basic operation for improving positioning accuracy by integrating positioning data with spatial road network data (roadway centerlines) to identify the correct road link on which a vehicle is travelling and to determine the location of a vehicle on a road link. The accuracy of the preliminary positioning result is always an issue for map-matching algorithms in all contexts, such as GPS data positioning, pedestrian positioning and navigation, mobile phone positioning and indoor positioning. Common map-matching methods are not effective for the positioning result with A-bis data from a Global System for Mobile Communication (GSM) cellular network system. This algorithm is employed to determine the road link on which a vehicle is located based on available mobile phone data from cellular network and GPS data. The evaluation of the HMM-based map-matching is performed with different levels of sampling GPS data and mobile phone data.
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