An intelligent mobile prediction method with mini-batch HTIA-based Seq2Seq networks

The prediction of individual mobility has been shown to hold significant commercial and social value in traffic planning and location advertising. However, the prediction of individual mobility is prone to substantial errors and high overhead time because of online learning or long input sequences. We propose an innovative sequence-to-sequence model with mini-batch hierarchical temporal incidence attention (HTIA) to address this issue. This model effectively captures long-term and short-term dependencies underlying individual mobility patterns. We perform minibatch training via sequence padding in HTIA to increase the model's efficiency while maintaining interpretability. Through extensive experiments conducted on three public datasets exhibiting different degrees of uncertainty, we demonstrated that our proposed approach outperforms state-of-the-art competing schemes, reducing the best mean relative error results by more than 70.8%, 60.8%, and 69.9%.