Holistic Semantic Representation for Navigational Trajectory Generation

2025-01-06

AI Summary

Existing trajectory generation methods employ limited semantic modeling, failing to jointly capture multi-scale spatiotemporal semantics—including road segments, geographic regions, individual trajectory points, and holistic trajectories—resulting in synthetic trajectories with insufficient realism and naturalness. To address this, we propose HOSER, a Hierarchical Omni-Scale Embedding Representation framework that unifies cross-granularity semantic modeling: from microscopic road nodes to macroscopic regions, and from local trajectory segments to global paths. Furthermore, we design a destination-guided navigator that synergistically integrates graph neural networks with sequential modeling to enable high-fidelity, origin-destination (OD)- and departure-time-conditioned trajectory generation. Evaluated on three real-world datasets, HOSER consistently outperforms state-of-the-art methods. Notably, it demonstrates robust generalization under few-shot and zero-shot settings, maintaining strong performance even with scarce or no task-specific training data.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Trajectory generation has garnered significant attention from researchers in the field of spatio-temporal analysis, as it can generate substantial synthesized human mobility trajectories that enhance user privacy and alleviate data scarcity. However, existing trajectory generation methods often focus on improving trajectory generation quality from a singular perspective, lacking a comprehensive semantic understanding across various scales. Consequently, we are inspired to develop a HOlistic SEmantic Representation (HOSER) framework for navigational trajectory generation. Given an origin-and-destination (OD) pair and the starting time point of a latent trajectory, we first propose a Road Network Encoder to expand the receptive field of road- and zone-level semantics. Second, we design a Multi-Granularity Trajectory Encoder to integrate the spatio-temporal semantics of the generated trajectory at both the point and trajectory levels. Finally, we employ a Destination-Oriented Navigator to seamlessly integrate destination-oriented guidance. Extensive experiments on three real-world datasets demonstrate that HOSER outperforms state-of-the-art baselines by a significant margin. Moreover, the model's performance in few-shot learning and zero-shot learning scenarios further verifies the effectiveness of our holistic semantic representation.

Problem

• Trajectory Generation
• Navigation Routes
• Realism and Naturalness

Innovation

• HOSER framework
• multi-granularity trajectory encoder
• data scarcity robustness