Name
Abstract | ||
|---|---|---|
We treat the problem of movement prediction as a classification task. We assume the existence of a (gradually populated and/or trained) knowledge base and try to compare the movement pattern of a certain object with stored information in order to predict its future location. We introduce a novel distance metric function based on weighted spatial and velocity context used for location prediction. The proposed distance metric is compared with other distance metrics in the literature on real traffic data and reveals its superiority. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s10776-014-0259-3 | IJWIN |
Keywords | Field | DocType |
Movement prediction, Sequential trajectory classification, Weighted spatial and velocity context | Data mining,Computer science,Metric (mathematics),Artificial intelligence,Knowledge base,Location prediction,Machine learning | Journal |
Volume | Issue | ISSN |
22 | 1 | 1572-8129 |
Citations | PageRank | References |
1 | 0.37 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Theodoros Anagnostopoulos | 1 | 157 | 14.54 |
| Christos-Nikolaos Anagnostopoulos | 2 | 1034 | 91.30 |
| Stathes Hadjiefthymiades | 3 | 809 | 76.76 |
| Arkady B. Zaslavsky | 4 | 943 | 168.27 |