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Predicting Human Mobility via Long Short-Term Patterns

Jianwei Chen, Jianbo Li*, Ying Li

College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China

* Corresponding Author: Jianbo Li. Email: email

Computer Modeling in Engineering & Sciences 2020, 124(3), 847-864. https://doi.org/10.32604/cmes.2020.010240

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.

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Chen, J., Li, J., Li, Y. (2020). Predicting Human Mobility via Long Short-Term Patterns. CMES-Computer Modeling in Engineering & Sciences, 124(3), 847–864.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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