PROBE VEHICLE TRACK-MATCHING ALGORITHM BASED on SPATIAL SEMANTIC FEATURES

Y. Luo, X. Song, L. Zheng, C. Yang, M. Yu, M. Sun

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

The matching of GPS received locations to roads is challenging. Traditional matching method is based on the position of the GPS receiver, the vehicle position and vehicle behavior near the receiving time. However, for probe vehicle trajectories, the sampling interval is too sparse and there is a poor correlation between adjacent sampling points, so it cannot partition the GPS noise through the historical positions. For the data mining of probe vehicle tracks based on spatial semantics, the matching is learned from the traditional electronic navigation map matching, and it is proposed that the probe vehicle track matching algorithm is based on spatial semantic features. Experimental results show that the proposed global-path matching method gets a good matching results, and restores the true path through the probe vehicle track.

Original languageEnglish (US)
Pages (from-to)19-23
Number of pages5
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume2
Issue number4W2
DOIs
StatePublished - Jul 10 2015
Event1st International Symposium on Spatiotemporal Computing, ISSC 2015 - Fairfax, United States
Duration: Jul 13 2015Jul 15 2015

Fingerprint

vehicles
probe
probes
Global positioning system
GPS
semantics
Semantics
Sampling
sampling
data mining
navigation
Data mining
vehicle
roads
Navigation
trajectory
Trajectories
partitions
road
receivers

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences (miscellaneous)
  • Environmental Science (miscellaneous)
  • Instrumentation

Cite this

@article{d35d0ea27d0b4ca69a658496dd57efc5,
title = "PROBE VEHICLE TRACK-MATCHING ALGORITHM BASED on SPATIAL SEMANTIC FEATURES",
abstract = "The matching of GPS received locations to roads is challenging. Traditional matching method is based on the position of the GPS receiver, the vehicle position and vehicle behavior near the receiving time. However, for probe vehicle trajectories, the sampling interval is too sparse and there is a poor correlation between adjacent sampling points, so it cannot partition the GPS noise through the historical positions. For the data mining of probe vehicle tracks based on spatial semantics, the matching is learned from the traditional electronic navigation map matching, and it is proposed that the probe vehicle track matching algorithm is based on spatial semantic features. Experimental results show that the proposed global-path matching method gets a good matching results, and restores the true path through the probe vehicle track.",
author = "Y. Luo and X. Song and L. Zheng and C. Yang and M. Yu and M. Sun",
year = "2015",
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PROBE VEHICLE TRACK-MATCHING ALGORITHM BASED on SPATIAL SEMANTIC FEATURES. / Luo, Y.; Song, X.; Zheng, L.; Yang, C.; Yu, M.; Sun, M.

In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 2, No. 4W2, 10.07.2015, p. 19-23.

Research output: Contribution to journalConference article

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T1 - PROBE VEHICLE TRACK-MATCHING ALGORITHM BASED on SPATIAL SEMANTIC FEATURES

AU - Luo, Y.

AU - Song, X.

AU - Zheng, L.

AU - Yang, C.

AU - Yu, M.

AU - Sun, M.

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N2 - The matching of GPS received locations to roads is challenging. Traditional matching method is based on the position of the GPS receiver, the vehicle position and vehicle behavior near the receiving time. However, for probe vehicle trajectories, the sampling interval is too sparse and there is a poor correlation between adjacent sampling points, so it cannot partition the GPS noise through the historical positions. For the data mining of probe vehicle tracks based on spatial semantics, the matching is learned from the traditional electronic navigation map matching, and it is proposed that the probe vehicle track matching algorithm is based on spatial semantic features. Experimental results show that the proposed global-path matching method gets a good matching results, and restores the true path through the probe vehicle track.

AB - The matching of GPS received locations to roads is challenging. Traditional matching method is based on the position of the GPS receiver, the vehicle position and vehicle behavior near the receiving time. However, for probe vehicle trajectories, the sampling interval is too sparse and there is a poor correlation between adjacent sampling points, so it cannot partition the GPS noise through the historical positions. For the data mining of probe vehicle tracks based on spatial semantics, the matching is learned from the traditional electronic navigation map matching, and it is proposed that the probe vehicle track matching algorithm is based on spatial semantic features. Experimental results show that the proposed global-path matching method gets a good matching results, and restores the true path through the probe vehicle track.

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