Striking a balance between diversity and regularity : a preference-guided transformer for individual mobility prediction

Abstract

Human mobility modeling and prediction are central research topics in GIScience. Although deep learning has led to significant advances in these fields, existing trajectory prediction models still face challenges in capturing the complexity of individual mobility behavior. Regression-based models often overestimate the diversity of human mobility, whereas classification models tend to underestimate it. This study attributes these biases to the models’ limitations in recognizing the spatial relationships among activity locations and mobility heterogeneity across individuals. To address these challenges, we propose the Spatial Preference Map-based Transformer (SPM-Former), explicitly integrating spatial proximity and mobility heterogeneity to enhance trajectory sequence prediction. To capture individual mobility characteristics, SPM-Former utilizes the Spatial Preference Map (SPM) to represent individuals’ spatial visitation preferences and adjacency relationships between locations. Then, we introduce two encoding modules to decode the information hidden within the SPM: one for encoding trajectory-level spatial-temporal information and another for embedding individual-level overall mobility features. Furthermore, we propose a novel optimization method, SPM-Loss, to assess prediction accuracy from the global spatial distribution perspective. Experimental results on a large-scale dataset from Japan demonstrate that SPM-Former outperforms state-of-the-art classification-based models, achieving approximately 3% and 20% improvements in trajectory sequence similarity and overall spatial feature similarity, respectively.