Name
Title | ||
|---|---|---|
TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks. |
Abstract | ||
|---|---|---|
Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing. We propose TrajectoryNet---a neural network architecture for point-based trajectory classification to infer real world human transportation modes from GPS traces. To overcome the challenge of capturing the underlying latent factors in the low-dimensional and heterogeneous feature space imposed by GPS data, we develop a novel representation that embeds the original feature space into another space that can be understood as a form of basis expansion. We also enrich the feature space via segment-based information and use Maxout activations to improve the predictive power of Recurrent Neural Networks (RNNs). We achieve over 98% classification accuracy when detecting four types of transportation modes, outperforming existing models without additional sensory data or location-based prior knowledge. |
Year | Venue | DocType |
|---|---|---|
2017 | CASCON | Conference |
Volume | Citations | PageRank |
abs/1705.02636 | 4 | 0.44 |
References | Authors | |
23 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang Jiang | 1 | 10 | 9.03 |
| Erico N. de Souza | 2 | 36 | 5.59 |
| Ahmad Pesaranghader | 3 | 28 | 4.20 |
| Baifan Hu | 4 | 8 | 1.21 |
| Daniel L. Silver | 5 | 272 | 33.36 |
| Stan Matwin | 6 | 3025 | 344.20 |