Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23461
Title: Map-matching algorithm for large-scale low-frequency floating car data
Authors: Chen, BY
Yuan, H
Li, Q
Lam, WHK 
Shaw, SL
Yan, K
Issue Date: 2014
Source: International journal of geographical information science, 2014, v. 28, no. 1, p. 22-38
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.
Keywords: Map matching
Mobile objects
Mobility
Publisher: Taylor & Francis
Journal: International journal of geographical information science 
ISSN: 1365-8816
EISSN: 1362-3087
DOI: 10.1080/13658816.2013.816427
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