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

Abstract

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.

Authors:
 [1];  [1];  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
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)
OSTI Identifier:
1414899
Report Number(s):
NREL/JA-5400-66848
Journal ID: ISSN 0361-1981
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Transportation Research Record: Journal of the Transportation Research Board
Additional Journal Information:
Journal Volume: 2645; Journal ID: ISSN 0361-1981
Publisher:
National Academy of Sciences, Engineering and Medicine
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; map matching; GPS; trajectory segmentation; longest common subsequence; LCS

Citation Formats

Zhu, Lei, Holden, Jacob R., and Gonder, Jeffrey D. Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data. United States: N. p., 2017. Web. doi:10.3141/2645-08.
Zhu, Lei, Holden, Jacob R., & Gonder, Jeffrey D. Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data. United States. https://doi.org/10.3141/2645-08
Zhu, Lei, Holden, Jacob R., and Gonder, Jeffrey D. 2017. "Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data". United States. https://doi.org/10.3141/2645-08. https://www.osti.gov/servlets/purl/1414899.
@article{osti_1414899,
title = {Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data},
author = {Zhu, Lei and Holden, Jacob R. and Gonder, Jeffrey D.},
abstractNote = {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.},
doi = {10.3141/2645-08},
url = {https://www.osti.gov/biblio/1414899}, journal = {Transportation Research Record: Journal of the Transportation Research Board},
issn = {0361-1981},
number = ,
volume = 2645,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2017},
month = {Sun Jan 01 00:00:00 EST 2017}
}

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Cited by: 23 works
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Works referenced in this record:

Some map matching algorithms for personal navigation assistants
journal, February 2000


Segment-Based Map Matching
conference, June 2007


Computing the FrÉChet Distance Between two Polygonal Curves
journal, March 1995


Addressing the Need for Map-Matching Speed: Localizing Globalb Curve-Matching Algorithms
conference, January 2006


A Map-Matching Method Using Intersection-Based Parallelogram Criterion
journal, November 2011


A Formal Basis for the Heuristic Determination of Minimum Cost Paths
journal, January 1968


Works referencing / citing this record:

Map-matching using shortest paths
conference, January 2018


Map Matching for Urban High-Sampling-Frequency GPS Trajectories
journal, January 2020


Map-Matching Using Shortest Paths
journal, February 2020