Name
Abstract | ||
|---|---|---|
The analysis of large amounts of multidimensional road traffic data for anomaly detection is a complex task. Visual analytics can bridge the gap between computational and human approaches to detecting anomalous behavior in road traffic, making the data analysis process more transparent. In this paper, we present a visual analytics framework that provides support for: 1) the exploration of multidim... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TITS.2017.2675710 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Roads,Data visualization,Data models,Accidents,Visual analytics,Vehicles,Data mining | Truck,Data modeling,Anomaly detection,Data mining,Data visualization,Simulation,Usability,Road traffic,Visual analytics,Engineering,Analytics | Journal |
Volume | Issue | ISSN |
18 | 8 | 1524-9050 |
Citations | PageRank | References |
2 | 0.36 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria Riveiro | 1 | 133 | 18.64 |
| Mikael Lebram | 2 | 68 | 9.52 |
| Marcus Elmer | 3 | 2 | 0.70 |