Learning Generalized and Flexible Trajectory Models from Omni-Semantic Supervision

2025-05-23

AI Summary

Existing trajectory retrieval methods suffer from low efficiency on large-scale data, limited support for conditional queries, and over-reliance on a single similarity metric. To address these issues, this paper proposes OmniTraj—a unified framework that jointly models four semantic modalities: trajectories, topological structures, road segments, and geographic regions. OmniTraj constructs a shared representation space through multimodal collaborative encoding and semantic alignment, enabling effective cross-modal feature fusion. Unlike conventional approaches, it supports flexible, interpretable retrieval driven by arbitrary single- or multi-modal conditions. Extensive experiments on two large-scale real-world trajectory datasets demonstrate significant improvements in both retrieval accuracy and efficiency, alongside strong scalability. OmniTraj thus provides a robust foundation for complex downstream applications requiring nuanced, context-aware trajectory analysis.

Technology Category

• Computer Vision: Multi-modal Vision
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The widespread adoption of mobile devices and data collection technologies has led to an exponential increase in trajectory data, presenting significant challenges in spatio-temporal data mining, particularly for efficient and accurate trajectory retrieval. However, existing methods for trajectory retrieval face notable limitations, including inefficiencies in large-scale data, lack of support for condition-based queries, and reliance on trajectory similarity measures. To address the above challenges, we propose OmniTraj, a generalized and flexible omni-semantic trajectory retrieval framework that integrates four complementary modalities or semantics -- raw trajectories, topology, road segments, and regions -- into a unified system. Unlike traditional approaches that are limited to computing and processing trajectories as a single modality, OmniTraj designs dedicated encoders for each modality, which are embedded and fused into a shared representation space. This design enables OmniTraj to support accurate and flexible queries based on any individual modality or combination thereof, overcoming the rigidity of traditional similarity-based methods. Extensive experiments on two real-world datasets demonstrate the effectiveness of OmniTraj in handling large-scale data, providing flexible, multi-modality queries, and supporting downstream tasks and applications.

Problem

• Inefficient large-scale trajectory data retrieval
• Lack of support for condition-based queries
• Over-reliance on trajectory similarity measures

Innovation

• Integrates four modalities into unified system
• Designs dedicated encoders for each modality
• Supports flexible multi-modality queries