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Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
2013 (English)In: ISPRS International Journal of Geo-Information, Vol. 2, no 2, 371-384 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2013. Vol. 2, no 2, 371-384 p.
Keyword [en]
Floating Car Data, Spatial Temporal Clusters
National Category
Geosciences, Multidisciplinary
URN: urn:nbn:se:kth:diva-166145DOI: 10.3390/ijgi2020371OAI: diva2:809369

QC 20150615

Available from: 2015-05-02 Created: 2015-05-02 Last updated: 2015-06-15Bibliographically approved

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Liu, XintaoBan, Yifang
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