Name
Abstract | ||
|---|---|---|
In this paper we propose a map matching method to overcoming the limitations of standard best-match reconstruction strategies. We use a more flexible approach which consider the k-optimal alternative paths to reconstruct the trajectories from the GPS raw data. The preliminary results, obtained on a real dataset of car users in Milan area, suggest that our method leads to beneficial effects on the successive analysis to be performed such as KNN and clustering. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICDMW.2009.62 | ICDM Workshops |
Keywords | Field | DocType |
standard best-match reconstruction strategy,gps raw data,k-optimal alternative path,milan area,real dataset,flexible approach,preliminary result,beneficial effect,trajectory data,k-bestmatch reconstruction,car user,successive analysis,color,trajectory,data mining,knn,data clustering,global positioning system,cartography | Data mining,Computer vision,Pattern clustering,Computer science,Raw data,Global Positioning System,Artificial intelligence,Cluster analysis,Machine learning,Map matching,Trajectory | Conference |
ISSN | Citations | PageRank |
2375-9232 | 3 | 0.46 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mirco Nanni | 1 | 1412 | 84.47 |
| Roberto Trasarti | 2 | 710 | 45.82 |