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:
-
- 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}
}
Web of Science
Works referenced in this record:
Some map matching algorithms for personal navigation assistants
journal, February 2000
- White, Christopher E.; Bernstein, David; Kornhauser, Alain L.
- Transportation Research Part C: Emerging Technologies, Vol. 8, Issue 1-6
Segment-Based Map Matching
conference, June 2007
- Chawathe, Sudarshan S.
- 2007 IEEE Intelligent Vehicles Symposium
Computing the FrÉChet Distance Between two Polygonal Curves
journal, March 1995
- Alt, Helmut; Godau, Michael
- International Journal of Computational Geometry & Applications, Vol. 05, Issue 01n02
Addressing the Need for Map-Matching Speed: Localizing Globalb Curve-Matching Algorithms
conference, January 2006
- Wenk, C.; Salas, R.; Pfoser, D.
- 18th International Conference on Scientific and Statistical Database Management (SSDBM'06)
A Map-Matching Method Using Intersection-Based Parallelogram Criterion
journal, November 2011
- Wang, Min; Bao, Xun; Zhu, Lei
- Advanced Materials Research, Vol. 403-408
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
journal, January 1968
- Hart, Peter; Nilsson, Nils; Raphael, Bertram
- IEEE Transactions on Systems Science and Cybernetics, Vol. 4, Issue 2
Works referencing / citing this record:
Map-matching using shortest paths
conference, January 2018
- Chambers, Erin; Fasy, Brittany Terese; Wang, Yusu
- Proceedings of the 3rd International Workshop on Interactive and Spatial Computing - IWISC '18
Map Matching for Urban High-Sampling-Frequency GPS Trajectories
journal, January 2020
- Liu, Minshi; Zhang, Ling; Ge, Junlian
- ISPRS International Journal of Geo-Information, Vol. 9, Issue 1
Map-Matching Using Shortest Paths
journal, February 2020
- Chambers, Erin; Fasy, Brittany Terese; Wang, Yusu
- ACM Transactions on Spatial Algorithms and Systems, Vol. 6, Issue 1