Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data

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40 Scopus Citations


With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory. The similarity of a trajectory segment and its matched path is described by a similarity score system based on the longest common subsequence. The numerical experiment indicated that the proposed map-matching algorithm was very promising in relation to accuracy and computational efficiency. Large-scale data set applications verified that the proposed method is robust and capable of dealing with real-world, large-scale GPS data in a computationally efficient and accurate manner.

Original languageAmerican English
Pages (from-to)67-75
Number of pages9
JournalTransportation Research Record
Issue number1
StatePublished - 2017

Bibliographical note

Publisher Copyright:
© 2017, SAGE Publications Ltd. All rights reserved.

NREL Publication Number

  • NREL/JA-5400-66848


  • GPS
  • LCS
  • longest common subsequence
  • map matching
  • trajectory segmentation


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