Name
Abstract | ||
|---|---|---|
Intelligent transportation systems often identify and make use of locations extracted from GPS trajectories to make informed decisions. However, many of the locations identified by existing systems are false positives, such as those in heavy traffic. Signals from the vehicle, such as speed and seatbelt status, can be used to identify these false positives. In this paper, we (i) demonstrate the utility of the Gradient-based Visit Extractor (GVE) in the automotive domain, (ii) propose a classification stage for removing false positives from the location extraction process, and (iii) evaluate the effectiveness of these techniques in a high resolution vehicular dataset.
|
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3281548.3281549 | GeoAI@SIGSPATIAL |
DocType | ISBN | Citations |
Conference | 978-1-4503-6036-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Van Hinsbergh | 1 | 0 | 0.34 |
| Nathan Griffiths | 2 | 115 | 15.49 |
| Phillip Taylor | 3 | 8 | 5.60 |
| Alasdair Thomason | 4 | 15 | 3.58 |
| Zhou Xu | 5 | 0 | 1.69 |
| Alexandros Mouzakitis | 6 | 86 | 12.60 |