Advanced search
1 file | 14.81 MB

Detecting road intersections from GPS traces using longest common subsequence algorithm

Xingzhe Xie (UGent) , Wenzhi Liao (UGent) , Hamid Aghajan (UGent) , Peter Veelaert (UGent) and Wilfried Philips (UGent)
Author
Organization
Abstract
Intersections are important components of road networks, which are critical to both route planning and path optimization. Most existing methods define the intersections as locations where the road users change their moving directions and identify the intersections from GPS traces through analyzing the road users’ turning behaviors. However, these methods suffer from finding an appropriate threshold for the moving direction change, leading to true intersections being undetected or spurious intersections being falsely detected. In this paper, the intersections are defined as locations that connect three or more road segments in different directions. We propose to detect the intersections under this definition by finding the common sub-tracks of the GPS traces. We first detect the Longest Common Subsequences (LCSS) between each pair of GPS traces using the dynamic programming approach. Second, we partition the longest nonconsecutive subsequences into consecutive sub-tracks. The starting and ending points of the common sub-tracks are collected as connecting points. At last, intersections are detected from the connecting points through Kernel Density Estimation (KDE). Experimental results show that our proposed method outperforms the turning point-based methods in terms of the F-score.
Keywords
intersection detection, road map inference, KDE, LCSS, GPS traces

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 14.81 MB

Citation

Please use this url to cite or link to this publication:

Chicago
Xie, Xingzhe, Wenzhi Liao, Hamid Aghajan, Peter Veelaert, and Wilfried Philips. 2017. “Detecting Road Intersections from GPS Traces Using Longest Common Subsequence Algorithm.” Isprs International Journal of Geo-information 6 (1).
APA
Xie, X., Liao, W., Aghajan, H., Veelaert, P., & Philips, W. (2017). Detecting road intersections from GPS traces using longest common subsequence algorithm. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 6(1).
Vancouver
1.
Xie X, Liao W, Aghajan H, Veelaert P, Philips W. Detecting road intersections from GPS traces using longest common subsequence algorithm. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION. MDPI AG; 2017;6(1).
MLA
Xie, Xingzhe, Wenzhi Liao, Hamid Aghajan, et al. “Detecting Road Intersections from GPS Traces Using Longest Common Subsequence Algorithm.” ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 6.1 (2017): n. pag. Print.
@article{8500793,
  abstract     = {Intersections are important components of road networks, which are critical to both route planning and path optimization. Most existing methods define the intersections as locations where the road users change their moving directions and identify the intersections from GPS traces through analyzing the road users{\textquoteright} turning behaviors. However, these methods suffer from finding an appropriate threshold for the moving direction change, leading to true intersections being undetected or spurious intersections being falsely detected. In this paper, the intersections are defined as locations
that connect three or more road segments in different directions. We propose to detect the intersections under this definition by finding the common sub-tracks of the GPS traces. We first detect the Longest Common Subsequences (LCSS) between each pair of GPS traces using the dynamic programming approach. Second, we partition the longest nonconsecutive subsequences into consecutive sub-tracks. The starting and ending points of the common sub-tracks are collected as connecting points. At last, intersections are detected from the connecting points through Kernel Density Estimation (KDE). Experimental results show that our proposed method outperforms the turning point-based methods
in terms of the F-score.},
  articleno    = {1},
  author       = {Xie, Xingzhe and Liao, Wenzhi and Aghajan, Hamid and Veelaert, Peter and Philips, Wilfried},
  issn         = {2220-9964},
  journal      = {ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION},
  keyword      = {intersection detection,road map inference,KDE,LCSS,GPS traces},
  language     = {eng},
  number       = {1},
  pages        = {15},
  publisher    = {MDPI AG},
  title        = {Detecting road intersections from GPS traces using longest common subsequence algorithm},
  url          = {http://dx.doi.org/10.3390/ijgi6010001},
  volume       = {6},
  year         = {2017},
}

Altmetric
View in Altmetric