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Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data.
- Source :
-
ISPRS International Journal of Geo-Information . Jun2013, Vol. 2 Issue 2, p371-384. 14p. - Publication Year :
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 22209964
- Volume :
- 2
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- ISPRS International Journal of Geo-Information
- Publication Type :
- Academic Journal
- Accession number :
- 89448558
- Full Text :
- https://doi.org/10.3390/ijgi2020371