Identification of Urban Network Congested Segments Using GPS Trajectories Double-Clustering Method

FU Zisheng; LI Qiuping; LIU Lin; ZHOU Suhong

doi:10.13203/j.whugis20150036

Volume 42 Issue 9

Sep. 2017

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Geomatics and Information Science of Wuhan University > 2017 > 42(9): 1264-1270. > DOI: 10.13203/j.whugis20150036

FU Zisheng, LI Qiuping, LIU Lin, ZHOU Suhong. Identification of Urban Network Congested Segments Using GPS Trajectories Double-Clustering Method[J]. Geomatics and Information Science of Wuhan University, 2017, 42(9): 1264-1270. DOI: 10.13203/j.whugis20150036

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Identification of Urban Network Congested Segments Using GPS Trajectories Double-Clustering Method

FU Zisheng^1,,
LI Qiuping^{1, 3},
LIU Lin^{1, 2, 3, ,},
ZHOU Suhong^{1, 3}

1.
School of Geography Science and Planning, Center of Integrated Geographic Information Anlaysis, Sun Yat-sen University, Guangzhou 510275, China
2.
Department of Geography, University of Cincinnati, OH 45221-0131, USA
3.
Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China

Funds:

The National Natural Science Foundation of China 41531178

The National Natural Science Foundation of China 41501424

the National Natural Science Foundation for Outstanding Youth 41522104

the Natural Science Foundation of Guangdong Province 2014A030312010

More Information

Author Bio:
FU Zisheng, master, specializes in the spatio-temporal analysis and modeling. E-mail:fzs1992@163.com
Corresponding author:
LIU Lin, PhD, professor. E-mail: liulin2@mail.sysu.edu.cn
Received Date: September 29, 2016
Published Date: September 04, 2017

Graphical Abstract

Abstract

Abstract

A double-clustering method is proposed for GPS trajectories in urban networks that efficiently identify trajectories with similar spatio-temporal and speed properties. The Spatio-temporal ranges of congested urban road segments are identified based on the trajectory velocity threshold. This paper defines similar queues and designs a DBSCAN based clustering method. By merging similar sub-trajectories into similar queues, the proposed method reduces computation costs. An improved trajectory similarity measure is used to calculate the spatio-temporal distance between similar queues. A C-means clustering method is applied. An experiment is implemented by clustering the floating car trajectories in Guangzhou, China; the results show that the proposed method can not only reflect the change of traffic congestion in space and time, but also improves computational efficiency when dealing with large-scale trajectory data.

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