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Detecting road intersections from GPS traces using longest common subsequence algorithm

Xingzhe Xie, Wenzhi Liao UGent, Hamid Aghajan UGent, Peter Veelaert UGent and Wilfried Philips UGent (2017) ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION. 6(1).
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
keyword
intersection detection, road map inference, KDE, LCSS, GPS traces
journal title
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
IJGI
volume
6
issue
1
article number
1
pages
15 pages
publisher
MDPI AG
ISSN
2220-9964
DOI
10.3390/ijgi6010001
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
8500793
handle
http://hdl.handle.net/1854/LU-8500793
alternative location
http://www.mdpi.com/2220-9964/6/1/1
http://www.mdpi.com/2220-9964/6/1/1
date created
2017-01-09 12:26:11
date last changed
2017-02-07 09:29:24
@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},
}

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.