Predicting human mobility via long short-term patterns

Abstract

Predicting human mobility has great significance in Location based Social Network applications, while it is challenging due to the impact of historical mobility patterns and current trajectories. Among these challenges, historical patterns tend to be crucial in the prediction task. However, it is difficult to capture complex patterns from long historical trajectories. Motivated by recent success of Convolutional Neural Network (CNN)-based methods, we propose a Union ConvGRU (UCG) Net, which can capture long short-term patterns of historical trajectories and sequential impact of current trajectories. Specifically, we first incorporate historical trajectories into hidden states by a shared-weight layer, and then utilize a 1D CNN to capture short-term pattern of hidden states. Next, an average pooling method is involved to generate separated hidden states of historical trajectories, on which we use a Fully Connected (FC) layer to capture long-term pattern subsequently. Finally, we use a Recurrent Neural Net-work (RNN) to predict future trajectories by integrating current trajectories and long short-term patterns. Experiments demonstrate that UCG Net performs best in comparison with neural network-based methods.