Objectives  Individual location prediction is significant in applications such as precise prevention, control of infectious diseases and scientific planning of public facilities. Existing location prediction algorithms mainly focus on mining and modeling individual longitudinal historical trajectory characteristics, and realize location prediction, and less consider the regular characteristics of users with horizontal similarity. Therefore, based on the framework of graph convolution network and long short-term memory (LSTM) model, this paper proposes an individual location prediction algorithm considering the characteristics of horizontally similar user trajectories and the characteristics of vertical historical regularity.

  Methods  First, we construct a user trajectory similarity algorithm, and select screen users with high similarity. Second, we use the graph convolution model to extract user trajectory features with high similarity. Finally, we use the LSTM model framework to extract historical trajectory features and integrate similar user trajectory features, so as to achieve individual location prediction.

  Results and Conclusions  Based on the data of more than 80 000 users in a city for 4 consecutive working days, the results show that the accuracy of the proposed algorithm decreases with the increase of the prediction time step, and the accuracy of night prediction is significantly higher than that of the day. Compared to the previous models, our proposed algorithm has an improvement of more than 10%. When 15 minutes is used as the prediction time step, the model accuracy rate reaches 80.45%.