%0 Journal Article
%T Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data
%A Xintao Liu
%A Yifang Ban
%J ISPRS International Journal of Geo-Information
%D 2013
%I MDPI AG
%R 10.3390/ijgi2020371
%X 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.
%K spatio-temporal cluster
%K floating car data
%K scaling and urban mobility patterns
%U http://www.mdpi.com/2220-9964/2/2/371