Name
Title | ||
|---|---|---|
Maritime Traffic Networks: From Historical Positioning Data to Unsupervised Maritime Traffic Monitoring. |
Abstract | ||
|---|---|---|
The large maritime traffic volume and its implications in economy, environment, safety, and security require an unsupervised system to monitor maritime traffic. In this paper, a method is proposed to automatically produce synthetic maritime traffic representations from historical self-reporting positioning data, more specifically from automatic identification system data. The method builds a two-l... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TITS.2017.2699635 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Artificial intelligence,Navigation,Surveillance,Security,Sensors,Data mining | Anomaly detection,Simulation,Baltic sea,Engineering,Automatic Identification System,Granularity,Traffic volume | Journal |
Volume | Issue | ISSN |
19 | 3 | 1524-9050 |
Citations | PageRank | References |
7 | 0.49 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Virginia Fernandez Arguedas | 1 | 27 | 4.20 |
| Giuliana Pallotta | 2 | 90 | 6.29 |
| Michele Vespe | 3 | 166 | 14.03 |