Private Cell-ID Trajectory Prediction Using Multi-Graph Embedding and Encoder-Decoder Network

Abstract

Trajectory prediction for mobile phone users is a cornerstone component to support many higher-level applications in LBSs (Location-Based Services). Most existing methods are designed based on the assumption that the explicit location information of the trajectories is available (e.g., GPS trajectories). However, collecting such kind of trajectories lays a heavy burden on the mobile phones and incurs privacy concerns. In this paper, we study the problem of trajectory prediction based on cell-id trajectories without explicit location information and propose a deep learning framework (called DeepCTP) to solve this problem. Specifically, we use a multi-graph embedding method to learn the latent spatial correlations between cell towers by exploiting handoff patterns. Then, we design a novel spatial-aware loss function for the encoder-decoder network to generate cell-id trajectory predictions. We conducted extensive experiments on real datasets. The experiment results show that DeepCTP outperforms the state-of-the-art cell-id trajectory prediction methods in terms of prediction error.