Poster: A Sequence-to-Sequence Model for Cell-ID Trajectory Prediction

It is expensive to collect trajectory data on a mobile phone by continuously pinpointing its location, which limits the application of trajectory data mining (e.g., trajectory prediction). In this poster, we propose a method for trajectory prediction by collecting cell-id trajectory data without explicit locations. First, it exploits the spatial correlation between cell towers based on graph embedding technique. Second, it employs the sequence-to-sequence (seq2seq) framework to train the prediction model by designing a novel spatial loss function. Experiment results based on real datasets have demonstrated the effectiveness of the proposed method.