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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.
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Acknowledgments
The authors are thankful to the anonymous referees for their comments and suggestions that improved the quality of this paper. The work described in this paper was jointly supported by Key Program of National Science Foundation of China (41231171), and research grants from National High-tech R&D Program of China (2012AA12A211), National Science Foundation of China (41201466, 41071285, 41021061), China Postdoctoral Science Foundation (2012M521469, 2013T60741), Shenzhen Scientific Research and Development Funding Program (ZDSY20121019111146499), Shenzhen Dedicated Funding of Strategic Emerging Industry Development Program (JCYJ20121019111128765) and the Research Grant Council of the Hong Kong Special Administration Region (PolyU 5196/10E).