A Convolutional Neural Network Approach for Modeling Semantic Trajectories and Predicting Future Locations

Abstract

In recent years, Location Based Service (LBS) providers rely increasingly on predictive models in order to offer their users timely and tailored solutions. Current location prediction algorithms go beyond using plain location data and show that additional context information can lead to a higher performance. Moreover, it has been shown that using semantics and projecting GPS trajectories on so called semantic trajectories can further improve the model. At the same time, Artificial Neural Networks (ANNs) have been proven to be very reliable when it comes to modeling and predicting time series. Recurrent network architectures show a particularly good performance. However, very little research has been done on the use of Convolutional Neural Networks (CNNs) in connection with modeling human movement patterns. In this work, we introduce a CNN-based approach for representing semantic trajectories and predicting future locations. Furthermore, we included an additional embedding layer to raise the efficiency. In order to evaluate our approach, we use the MIT Reality Mining dataset and use a Feed-Forward (FFNN) -, a Recurrent (RNN) - and a LSTM network to compare it with on two different semantic trajectory levels. We show that CNNs are more than capable of handling semantic trajectories, while providing high prediction accuracies at the same time.