Name
Title | ||
|---|---|---|
Reusability of the Output of Map-Matching Algorithms Across Space and Time Through Machine Learning. |
Abstract | ||
|---|---|---|
A map-matching algorithm outputs a vector per GPS point, projecting the moving object on one of the segments of the transportation network. Although developing more sophisticated map-matching algorithms for vehicle and pedestrian navigation systems have been the focus of research in this field, reusability of the historical information already provided by map-matching algorithms has not been addre... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TITS.2017.2669085 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Global Positioning System,Machine learning algorithms,Encoding,Training,Satellites,Receivers | Nonlinear system,Computer science,Multilayer perceptron,Artificial intelligence,Global Positioning System,Linear least squares,Flow network,Computer vision,Simulation,Algorithm,Map matching,Machine learning,Reusability,Encoding (memory) | Journal |
Volume | Issue | ISSN |
18 | 11 | 1524-9050 |
Citations | PageRank | References |
2 | 0.66 | 11 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahdi Hashemi | 1 | 53 | 9.43 |