Personal location prediction algorithm taking into account similar user characteristics

Abstract

Individual location prediction is of great significance in applications such as precise prevention and 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. For this reason, based on the framework of Graph Convolution Network (GCN) and Long Short-Term Memory (LSTM) model, this paper proposes an individual location prediction algorithm that takes into account the characteristics of horizontally similar user trajectories and the characteristics of vertical historical regularity. First, construct a user trajectory similarity algorithm and screen users with high similarity, then use the graph convolution model to extract user trajectory features with high similarity of users to be predicted, and finally use the long and short-term memory model framework to extract historical trajectory features and integrate similar user trajectory features, So as to achieve individual location prediction. 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 method proposed in this paper decreases with the increase of the prediction time step, and the accuracy of night prediction is significantly higher than that of the day, but compared to the previous All models have an improvement of more than 10%. When 15 minutes is used as the prediction time step, the model accuracy rate reaches 80.45%.