GSTM-HMU: Generative Spatio-Temporal Modeling for Human Mobility Understanding

2025-09-23

AI Summary

Existing methods struggle to jointly model the semantic–temporal characteristics of both short-term visiting patterns and long-term lifestyle regularities in human mobility trajectories. To address this, we propose a generative spatiotemporal modeling framework comprising a spatiotemporal concept encoder, a cognitive trajectory memory, a lifestyle concept repository, and a multi-task generative head—enabling, for the first time, unified modeling of geographic coordinates, POI semantics, periodic temporal rhythms, historical behaviors, and structured preferences. The framework supports downstream tasks including next-location prediction, trajectory-based user identification, and arrival-time estimation, while ensuring interpretability, personalization, and strong generalization. Extensive experiments on four real-world datasets—Gowalla, WeePlace, Brightkite, and Foursquare—demonstrate significant improvements over state-of-the-art methods, validating its effectiveness and robustness in capturing complex mobility patterns.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Human mobility traces, often recorded as sequences of check-ins, provide a unique window into both short-term visiting patterns and persistent lifestyle regularities. In this work we introduce GSTM-HMU, a generative spatio-temporal framework designed to advance mobility analysis by explicitly modeling the semantic and temporal complexity of human movement. The framework consists of four key innovations. First, a Spatio-Temporal Concept Encoder (STCE) integrates geographic location, POI category semantics, and periodic temporal rhythms into unified vector representations. Second, a Cognitive Trajectory Memory (CTM) adaptively filters historical visits, emphasizing recent and behaviorally salient events in order to capture user intent more effectively. Third, a Lifestyle Concept Bank (LCB) contributes structured human preference cues, such as activity types and lifestyle patterns, to enhance interpretability and personalization. Finally, task-oriented generative heads transform the learned representations into predictions for multiple downstream tasks. We conduct extensive experiments on four widely used real-world datasets, including Gowalla, WeePlace, Brightkite, and FourSquare, and evaluate performance on three benchmark tasks: next-location prediction, trajectory-user identification, and time estimation. The results demonstrate consistent and substantial improvements over strong baselines, confirming the effectiveness of GSTM-HMU in extracting semantic regularities from complex mobility data. Beyond raw performance gains, our findings also suggest that generative modeling provides a promising foundation for building more robust, interpretable, and generalizable systems for human mobility intelligence.

Problem

• Modeling semantic and temporal complexity of human movement patterns
• Predicting future locations and identifying users from mobility traces
• Extracting lifestyle regularities from complex spatio-temporal check-in data

Innovation

• Spatio-temporal encoder integrates location, semantics and temporal rhythms
• Cognitive memory filters historical visits to capture user intent
• Lifestyle concept bank adds structured preference cues for personalization