A Graph Neural Network Models Incorporating Spatial and Temporal Information for Next-Location Prediction

With the rapid growth of smart devices and positioning technologies, spatiotemporal data has become essential for predicting user behavior. However, many existing next-location prediction models employ oversimplified temporal modeling, neglect spatial structure and semantic relationships, and fail to capture complex location interaction patterns. This study proposes a graph neural network model that integrates spatiotemporal features to enhance next-location prediction. There are three components in the proposed method, the first is location feature representation which combines geocodes and location category embeddings to construct semantically enriched node representations. The second is temporal modeling which computes temporal similarity between historical trajectories and current behaviors to generate time-decay weights, thereby capturing behavioral periodicity and preference shifts. The third is preference integration which long-term historical preferences and short-term current preferences are modeled using a long short-term memory (LSTM) network and subsequently fused with spatial preferences to generate a comprehensive semantic representation encompassing both user preferences and location characteristics. Experiments on real-world trajectory datasets demonstrate that our proposed model achieves superior accuracy compared to state-of-the-art approaches in next-location prediction.