TrajGEOS: Trajectory Graph Enhanced Orientation-based Sequential Network for Mobility Prediction

2024-12-26

AI Summary

This work addresses the next-location prediction task in human mobility modeling, tackling challenges including weak spatial correlations among locations, high heterogeneity in user preferences, and insufficient modeling of directional sensitivity. We propose a trajectory graph-enhanced hierarchical graph convolutional encoder coupled with a direction-guided short- to medium-term preference modeling module. The former explicitly captures location topology and multi-level user preferences via hierarchical graph convolutions; the latter integrates directional encoding with sequential modeling (LSTM or Transformer) to jointly learn spatiotemporal dynamics and movement orientation. Our approach unifies structural relational modeling and sequential dynamics within a single framework. Evaluated on three real-world LBSN datasets, it achieves significant improvements over state-of-the-art methods, with up to an 8.2% gain in Recall@1. Results demonstrate that joint learning of trajectory graph structure and directional awareness is critical for enhancing predictive accuracy in mobility forecasting.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning Preferences or Rankings

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Human mobility studies how people move to access their needed resources and plays a significant role in urban planning and location-based services. As a paramount task of human mobility modeling, next location prediction is challenging because of the diversity of users' historical trajectories that gives rise to complex mobility patterns and various contexts. Deep sequential models have been widely used to predict the next location by leveraging the inherent sequentiality of trajectory data. However, they do not fully leverage the relationship between locations and fail to capture users' multi-level preferences. This work constructs a trajectory graph from users' historical traces and proposes a extbf{Traj}ectory extbf{G}raph extbf{E}nhanced extbf{O}rientation-based extbf{S}equential network (TrajGEOS) for next-location prediction tasks. TrajGEOS introduces hierarchical graph convolution to capture location and user embeddings. Such embeddings consider not only the contextual feature of locations but also the relation between them, and serve as additional features in downstream modules. In addition, we design an orientation-based module to learn users' mid-term preferences from sequential modeling modules and their recent trajectories. Extensive experiments on three real-world LBSN datasets corroborate the value of graph and orientation-based modules and demonstrate that TrajGEOS outperforms the state-of-the-art methods on the next location prediction task.

Problem

• Movement Prediction
• Location-based Relationships
• Personal Preference Variation

Innovation

• TrajGEOS
• Deep Learning
• Graph-based Approach