Unifying Human Mobility Forecasting and Trajectory Semantics Augmentation via Hawkes Process Based LSTM

Human mobility forecasting can help us understand human movements, improve urban planning, and, ultimately, promote the development of livable, sustainable, and viable communities. While some efforts have been made for forecasting traffic or annotating trajectories, existing methods can be improved via simultaneously conducting human mobility forecasting and trajectory semantics augmentation. Along this line, in this paper, we provide a joint perspective of point processes and sequential embedding, in order to unify mobility arrival forecasting and trajectory semantics augmentation in a Hawkes-based long short-term memory (LSTM) method. Specifically, we first regard the traffic trajectories of a region as an arrival sequence according to the arrival time. Besides, we develop a method that exploits the mutual information of Hawkes processes and LSTM to model the arrival sequences of each region. Particularly, Hawkes processes predict the time and intensities of upcoming mobility arrivals; LSTM learns the embedding of arrivals, and annotates the arrival destinations and trip purposes; the mobility arrival intensities in Hawkes processes are influenced by the hidden states of LSTM. As applications, we exploit the proposed method to predict 3W (when, where, what) and discover functional regions. Finally, extensive experimental results with real-world traffic trajectory data demonstrate the enhanced performances of our method.