@Article{cmes.2020.010240,
AUTHOR = {Jianwei Chen, Jianbo Li, Ying Li},
TITLE = {Predicting Human Mobility via Long Short-Term Patterns},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {124},
YEAR = {2020},
NUMBER = {3},
PAGES = {847--864},
URL = {http://www.techscience.com/CMES/v124n3/39914},
ISSN = {1526-1506},
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 longterm 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.},
DOI = {10.32604/cmes.2020.010240}
}