Journal of System Simulation
Abstract
Abstract: In view of the incapability of traditional methods to efficiently process massive trajectory data, an improved HMM (hidden-Markov model) map matching algorithm is proposed. Spatial index for road networks is established through R-tree spatial index. GPS trajectory data are segmented based on the position change rates of trajectory points. R-tree index is used to quickly determine the candidate road section that sub-trajectories belong to, and the key points of the sub-trajectories instead of the entire sub-trajectories are selected to judge which road the sub-trajectories should be matched with. The map matching of each sub-trajectory is carried out on the basis of the former results. The algorithm is verified by a simulation experiment using Beijing's floating car data and OpenStreetMap data. Experimental result shows that the proposed algorithm can reduce the workload of road search and trajectory point traversal and can greatly improve the algorithmic efficiency.
Recommended Citation
Song, Yanjiao; Zhou, Jiayue; Wang, Longhao; Wu, Jing; Li, Rui; and Rui, Xiaoping
(2023)
"Efficient HMM Map Matching Method Using R-tree and Trajectory Segmentation,"
Journal of System Simulation: Vol. 35:
Iss.
2, Article 11.
DOI: 10.16182/j.issn1004731x.joss.21-1020
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss2/11
First Page
339
Revised Date
2021-12-21
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.21-1020
Last Page
349
CLC
TP310.6
Recommended Citation
Yanjiao Song, Jiayue Zhou, Longhao Wang, Jing Wu, Rui Li, Xiaoping Rui. Efficient HMM Map Matching Method Using R-tree and Trajectory Segmentation[J]. Journal of System Simulation, 2023, 35(2): 339-349.
DOI
10.16182/j.issn1004731x.joss.21-1020
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