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

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.
 [1] ;  [1] ;  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Report Number(s):
Journal ID: ISSN 0361-1981
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Transportation Research Record
Additional Journal Information:
Journal Volume: 2645; Journal ID: ISSN 0361-1981
National Academy of Sciences, Engineering and Medicine
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), NREL Laboratory Directed Research and Development (LDRD)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; map matching; GPS; trajectory segmentation; longest common subsequence; LCS
OSTI Identifier: