Map-matching algorithm for large-scale low-frequency floating car data

Large-scale global positioning system (GPS) positioning information of floating cars has been recognised as a major data source for many transportation applications. Mapping large-scale low-frequency floating car data (FCD) onto the road network is very challenging for traditional map-matching (MM)...

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Published inInternational journal of geographical information science : IJGIS Vol. 28; no. 1; pp. 22 - 38
Main Authors Chen, Bi Yu, Yuan, Hui, Li, Qingquan, Lam, William H.K., Shaw, Shih-Lung, Yan, Ke
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 02.01.2014
Taylor & Francis LLC
Subjects
Accuracy
Algorithms
Dynamic programming
Global positioning systems
GPS
map matching
mobile objects
mobility
Online AccessGet full text
ISSN1365-8816
1362-3087
1365-8824
DOI10.1080/13658816.2013.816427

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Abstract Large-scale global positioning system (GPS) positioning information of floating cars has been recognised as a major data source for many transportation applications. Mapping large-scale low-frequency floating car data (FCD) onto the road network is very challenging for traditional map-matching (MM) algorithms developed for in-vehicle navigation. In this paper, a multi-criteria dynamic programming map-matching (MDP-MM) algorithm is proposed for online matching FCD. In the proposed MDP-MM algorithm, the MDP technique is used to minimise the number of candidate routes maintained at each GPS point, while guaranteeing to determine the best matching route. In addition, several useful techniques are developed to improve running time of the shortest path calculation in the MM process. Case studies based on real FCD demonstrate the accuracy and computational performance of the MDP-MM algorithm. Results indicated that the MDP-MM algorithm is competitive with existing algorithms in both accuracy and computational performance.
AbstractList Large-scale global positioning system (GPS) positioning information of floating cars has been recognised as a major data source for many transportation applications. Mapping large-scale low-frequency floating car data (FCD) onto the road network is very challenging for traditional map-matching (MM) algorithms developed for in-vehicle navigation. In this paper, a multi-criteria dynamic programming map-matching (MDP-MM) algorithm is proposed for online matching FCD. In the proposed MDP-MM algorithm, the MDP technique is used to minimise the number of candidate routes maintained at each GPS point, while guaranteeing to determine the best matching route. In addition, several useful techniques are developed to improve running time of the shortest path calculation in the MM process. Case studies based on real FCD demonstrate the accuracy and computational performance of the MDP-MM algorithm. Results indicated that the MDP-MM algorithm is competitive with existing algorithms in both accuracy and computational performance. [PUBLICATION ABSTRACT]
Large-scale global positioning system (GPS) positioning information of floating cars has been recognised as a major data source for many transportation applications. Mapping large-scale low-frequency floating car data (FCD) onto the road network is very challenging for traditional map-matching (MM) algorithms developed for in-vehicle navigation. In this paper, a multi-criteria dynamic programming map-matching (MDP-MM) algorithm is proposed for online matching FCD. In the proposed MDP-MM algorithm, the MDP technique is used to minimise the number of candidate routes maintained at each GPS point, while guaranteeing to determine the best matching route. In addition, several useful techniques are developed to improve running time of the shortest path calculation in the MM process. Case studies based on real FCD demonstrate the accuracy and computational performance of the MDP-MM algorithm. Results indicated that the MDP-MM algorithm is competitive with existing algorithms in both accuracy and computational performance.
Author Lam, William H.K.
Li, Qingquan
Chen, Bi Yu
Yuan, Hui
Shaw, Shih-Lung
Yan, Ke
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  organization: International School of Software, Wuhan University
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Snippet Large-scale global positioning system (GPS) positioning information of floating cars has been recognised as a major data source for many transportation...
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SubjectTerms Accuracy
Algorithms
Dynamic programming
Global positioning systems
GPS
map matching
mobile objects
mobility
Title Map-matching algorithm for large-scale low-frequency floating car data
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