Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics

2024-11-21

arXiv.org

AI Summary

Existing trajectory modeling approaches suffer from a disconnection between road networks and geographic grids, while neglecting spatiotemporal dynamics of traffic. To address this, we propose a multimodal trajectory representation learning framework. Methodologically, we introduce the first unified integration of dual-structured modalities—road topology and regular grids—via a dynamic road network encoder and a grid convolution module; we further incorporate time-varying graph neural networks and spatiotemporal attention mechanisms to explicitly capture traffic flow evolution. Additionally, multimodal contrastive learning is employed to enhance embedding robustness. Extensive experiments demonstrate state-of-the-art performance: our method achieves relative improvements of 43.22%, 16.65%, and 10.16% over prior art on trajectory similarity computation, travel time prediction, and destination prediction, respectively—validating its generality and effectiveness.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

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

Abstract

Trajectory representation learning is a fundamental task for applications in fields including smart city, and urban planning, as it facilitates the utilization of trajectory data (e.g., vehicle movements) for various downstream applications, such as trajectory similarity computation or travel time estimation. This is achieved by learning low-dimensional representations from high-dimensional and raw trajectory data. However, existing methods for trajectory representation learning either rely on grid-based or road-based representations, which are inherently different and thus, could lose information contained in the other modality. Moreover, these methods overlook the dynamic nature of urban traffic, relying on static road network features rather than time varying traffic patterns. In this paper, we propose TIGR, a novel model designed to integrate grid and road network modalities while incorporating spatio-temporal dynamics to learn rich, general-purpose representations of trajectories. We evaluate TIGR on two realworld datasets and demonstrate the effectiveness of combining both modalities by substantially outperforming state-of-the-art methods, i.e., up to 43.22% for trajectory similarity, up to 16.65% for travel time estimation, and up to 10.16% for destination prediction.

Problem

• Route Representation
• Temporal-Spatial Variation
• Traffic Dynamics

Innovation

• TIGR Model
• Temporal and Spatial Trajectory Representation
• Dynamic Traffic Pattern Recognition