Using Deep Learning for Trajectory Classification

The ubiquity of GPS-enabled smartphones and automotive navigation systems connected to the Internet allows us to monitor, collect, and analyze large trajectory data streams in real-time. Trajectory classification is an efficient way to analyze trajectory, consisting of building a prediction model to classify a new trajectory (or sub-trajectory) in a single-class or multi-class. The classification trajectory problem is challenging because of the massive volume of trajectory data, the complexity associated with the data representation, the sparse nature of the spatio-temporal points, the multidimensionality, and the number of classes can be much larger than the number of motion patterns. Machine learning methods can handle trajectories, but they demand a feature extraction process, and they suffer from the curse of dimensionality. On the other hand, more recent Deep Learning models emerged to link trajectories to their generating users. Although they minimize the sparsity problem by representing the input data as an embedding vector, these models limit themselves to deal with multidimensional data. In this paper, we propose DeepeST (Deep Learning for Sub-Trajectory classification) to identify the category from a large number of sub-trajectories extracted from GPS services and check-ins data. DeepeST employs a Recurrent Neural Network (RNN), both LSTM and Bi-directional LSTM (BLSTM), which operates on the low-dimensional to learn the underlying category. We tackled the classification problem and conducted experiments on three real datasets with trajectories from GPS services and check-ins. We show that DeepeST outperforms machine learning approaches and deep learning approaches from state-of-the-art.