Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.3390/ijgi2020371 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION |
Keywords | Field | DocType |
spatio-temporal cluster,floating car data,scaling and urban mobility patterns | Cluster (physics),Road networks,Pareto distribution,Simulation,Floating car data,Global Positioning System,Complex network,Statistics,Scaling,Geography,Traffic congestion | Journal |
Volume | Issue | ISSN |
2 | 2 | 2220-9964 |
Citations | PageRank | References |
9 | 0.68 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xintao Liu | 1 | 70 | 15.24 |
| Yifang Ban | 2 | 194 | 22.83 |