ISPRS Int. J. Geo-Inf. 2013, 2(2), 371-384; doi:10.3390/ijgi2020371
Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data
Division of Geoinformatics, Royal Institute of Technology (KTH), Stockholm SE-10044, Sweden
*
Author to whom correspondence should be addressed.
Received: 20 March 2013 / Revised: 22 April 2013 / Accepted: 2 May 2013 / Published: 10 May 2013
Abstract
In this paper, we explore spatio-temporal clusters using massive floating car data from a complex network perspective. We analyzed over 85 million taxicab GPS points (floating car data) collected in Wuhan, Hubei, China. Low-speed and stop points were selected to generate spatio-temporal clusters, which indicated the typical stop-and-go movement pattern in real-world traffic congestion. We found that the sizes of spatio-temporal clusters exhibited a power law distribution. This implies the presence of a scaling property; i.e., they can be naturally divided into a strong hierarchical structure: long time-duration ones (a low percentage) whose values lie above the mean value and short ones (a high percentage) whose values lie below. The spatio-temporal clusters at different levels represented the degree of traffic congestions, for example the higher the level, the worse the traffic congestions. Moreover, the distribution of traffic congestions varied spatio-temporally and demonstrated a multinuclear structure in urban road networks, which suggested there is a correlation to the corresponding internal mobile regularities of an urban system.
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
Scifeed alert for new publications
Never miss any articles matching your research from any publisher- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Share & Cite This Article
MDPI and ACS Style
Liu, X.; Ban, Y. Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data. ISPRS Int. J. Geo-Inf. 2013, 2, 371-384.
Related Articles
Article Metrics
Comments
[Return to top]
ISPRS Int. J. Geo-Inf.
EISSN 2220-9964
Published by MDPI AG, Basel, Switzerland
RSS
E-Mail Table of Contents Alert