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

Bi Yu Chen, Hui Yuan, Qingquan Li, Hing Keung William Lam, Shih Lung Shaw, Ke Yan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

155 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)22-38
Number of pages17
JournalInternational Journal of Geographical Information Science
Issue number1
Publication statusPublished - 1 Jan 2014


  • map matching
  • mobile objects
  • mobility

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences


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