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Understanding and discovering knowledge from GPS (Global Positioning System)traces of human activities is an essential topic in mobility-based urbancomputing. We propose TrajectoryNet-a neural network architecture forpoint-based trajectory classification to infer real world human transportationmodes from GPS traces. To overcome the challenge of capturing the underlyinglatent factors in the low-dimensional and heterogeneous feature space imposedby GPS data, we develop a novel representation that embeds the original featurespace 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 Maxoutactivations to improve the predictive power of Recurrent Neural Networks(RNNs). We achieve over 98% classification accuracy when detecting four typesof transportation modes, outperforming existing models without additionalsensory data or location-based prior knowledge.

TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks