  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.
