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Scipediacontent moved page Draft Content 103201244 to 238,295fh

Scipediacontent: Created page with " == 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..."

2021-03-22T20:01:00Z

Created page with " == 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..."

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== 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.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_103201244-beopen1163-3838-document.pdf</pdf>

== Original document ==

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

* [http://dx.doi.org/10.3390/ijgi2020371 http://dx.doi.org/10.3390/ijgi2020371] under the license https://creativecommons.org/licenses/by

* [http://dx.doi.org/10.3390/ijgi2020371 http://dx.doi.org/10.3390/ijgi2020371] under the license http://creativecommons.org/licenses/by/3.0/

* [https://www.mdpi.com/2220-9964/2/2/371/pdf https://www.mdpi.com/2220-9964/2/2/371/pdf] under the license https://creativecommons.org/licenses/by/4.0

* [http://www.mdpi.com/2220-9964/2/2/371 http://www.mdpi.com/2220-9964/2/2/371],
: [https://doaj.org/toc/2220-9964 https://doaj.org/toc/2220-9964] under the license cc-by

* [http://www.mdpi.com/2220-9964/2/2/371/pdf http://www.mdpi.com/2220-9964/2/2/371/pdf],
: [http://dx.doi.org/10.3390/ijgi2020371 http://dx.doi.org/10.3390/ijgi2020371]

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

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Scipediacontent: Scipediacontent moved page Draft Content 103201244 to 238,295fh

Scipediacontent: Created page with " == 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..."