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

Zhu, Lei; Holden, Jacob R.; Gonder, Jeffrey D.

doi:10.3141/2645-08

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:

Zhu, Lei ^[1]; Holden, Jacob R. ^[1]; Gonder, Jeffrey D. ^[1]

National Renewable Energy Lab. (NREL), Golden, CO (United States)

Publication Date:: Sun Jan 01 00:00:00 EST 2017

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:: 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.

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                    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

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                    Zhu, Lei, Holden, Jacob R., and Gonder, Jeffrey D. Sun .  
"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.

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@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},

  journal      = {Transportation Research Record: Journal of the Transportation Research Board},

  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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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
DOI: 10.1016/s0968-090x(00)00026-7

Segment-Based Map Matching
conference, June 2007

Chawathe, Sudarshan S.
2007 IEEE Intelligent Vehicles Symposium
DOI: 10.1109/ivs.2007.4290280

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
DOI: 10.1142/s0218195995000064

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)
DOI: 10.1109/SSDBM.2006.11

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

Wang, Min; Bao, Xun; Zhu, Lei
Advanced Materials Research, Vol. 403-408
DOI: 10.4028/www.scientific.net/AMR.403-408.2746

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
DOI: 10.1109/TSSC.1968.300136

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
DOI: 10.1145/3191801.3191812

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
DOI: 10.3390/ijgi9010031

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
DOI: 10.1145/3368617

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Similar Records

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

Abstract

Citation Formats

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

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

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

Map-matching using shortest paths
conference, January 2018

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

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journal, February 2020