Name
Abstract | ||
|---|---|---|
Several algorithms have been proposed in the last few years for mining different mobility patterns from trajectories, such as flocks, chasing, meeting, and convergence. An interesting behavior that has not been much explored in trajectory pattern mining is avoidance. In this paper we define the avoidance behavior between moving object trajectories, providing a set of theoretical definitions to precisely describe various kinds of avoidance, and propose an effective algorithm for detecting avoidances. The proposed method is quantitatively evaluated on a real-world dataset, and correctly detects with high precision the quasi totality of the trajectory pairs that exhibit avoidance behaviors (F-measure up to 95%). |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.datak.2015.12.003 | Data & Knowledge Engineering |
Keywords | Field | DocType |
Trajectory avoidance detection,Spatio-temporal data analysis,Trajectory data mining,Moving object behavior analysis | Convergence (routing),Data mining,Trajectory data mining,Computer science,Trajectory | Journal |
Volume | Issue | ISSN |
102 | C | 0169-023X |
Citations | PageRank | References |
2 | 0.37 | 20 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Lettich | 1 | 19 | 5.37 |
| Luis Otávio Alvares | 2 | 595 | 35.18 |
| Vania Bogorny | 3 | 828 | 46.56 |
| Salvatore Orlando | 4 | 1595 | 202.29 |
| Alessandra Raffaetà | 5 | 187 | 16.45 |
| Claudio Silvestri | 6 | 136 | 10.21 |