ISPRS Int. J. Geo-Inf. 2013, 2(2), 371-384; doi:10.3390/ijgi2020371
Article

Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data

* email and email
Received: 20 March 2013; in revised form: 22 April 2013 / Accepted: 2 May 2013 / Published: 10 May 2013
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords: spatio-temporal cluster; floating car data; scaling and urban mobility patterns
PDF Full-text Download PDF Full-Text [613 KB, uploaded 10 May 2013 18:35 CEST]

Export to BibTeX |
EndNote


MDPI and ACS Style

Liu, X.; Ban, Y. Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data. ISPRS Int. J. Geo-Inf. 2013, 2, 371-384.

AMA Style

Liu X, Ban Y. Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data. ISPRS International Journal of Geo-Information. 2013; 2(2):371-384.

Chicago/Turabian Style

Liu, Xintao; Ban, Yifang. 2013. "Uncovering Spatio-Temporal Cluster Patterns Using Massive Floating Car Data." ISPRS Int. J. Geo-Inf. 2, no. 2: 371-384.

ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert