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.

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The different versions of the original document can be found in:

https://doaj.org/toc/2220-9964 under the license cc-by
http://dx.doi.org/10.3390/ijgi2020371
https://www.mdpi.com/2220-9964/2/2/371/pdf,
http://www.diva-portal.org/smash/record.jsf?pid=diva2:809369,
http://ui.adsabs.harvard.edu/abs/2013IJGI....2..371L/abstract,
https://dblp.uni-trier.de/db/journals/ijgi/ijgi2.html#LiuB13,
https://dx.doi.org/10.3390/ijgi2020371,
http://dx.doi.org/10.3390/ijgi2020371,
https://academic.microsoft.com/#/detail/2144194027 under the license https://creativecommons.org/licenses/by/4.0/
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Published on 01/01/2013

Volume 2013, 2013
DOI: 10.3390/ijgi2020371
Licence: Other

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