Dual-branch Spatial-Temporal Self-supervised Representation for Enhanced Road Network Learning

2025-11-10

AI Summary

Existing GNN- and contrastive learning–based approaches for road network representation struggle to jointly capture spatial heterogeneity and temporal dynamics, primarily due to the oversimplified neighborhood smoothing assumption. To address this, we propose DST, a dual-branch spatiotemporal self-supervised framework. First, DST constructs three types of hyperedges to form a hypergraph, enabling hypergraph contrastive learning to model complex, heterogeneous spatial relationships. Second, it designs a hybrid hop-aware transition matrix to encode trajectory dynamics and incorporates a causal Transformer—distinguished by weekday/weekend contexts—for time-series forecasting, thereby achieving traffic-pattern-aware zero-shot generalization. Evaluated on multiple benchmarks, DST significantly outperforms state-of-the-art methods, especially under zero-shot settings, demonstrating superior robustness. These results validate both the effectiveness and necessity of joint spatiotemporal modeling for road representation learning.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Road network representation learning (RNRL) has attracted increasing attention from both researchers and practitioners as various spatiotemporal tasks are emerging. Recent advanced methods leverage Graph Neural Networks (GNNs) and contrastive learning to characterize the spatial structure of road segments in a self-supervised paradigm. However, spatial heterogeneity and temporal dynamics of road networks raise severe challenges to the neighborhood smoothing mechanism of self-supervised GNNs. To address these issues, we propose a $ extbf{D}$ual-branch $ extbf{S}$patial-$ extbf{T}$emporal self-supervised representation framework for enhanced road representations, termed as DST. On one hand, DST designs a mix-hop transition matrix for graph convolution to incorporate dynamic relations of roads from trajectories. Besides, DST contrasts road representations of the vanilla road network against that of the hypergraph in a spatial self-supervised way. The hypergraph is newly built based on three types of hyperedges to capture long-range relations. On the other hand, DST performs next token prediction as the temporal self-supervised task on the sequences of traffic dynamics based on a causal Transformer, which is further regularized by differentiating traffic modes of weekdays from those of weekends. Extensive experiments against state-of-the-art methods verify the superiority of our proposed framework. Moreover, the comprehensive spatiotemporal modeling facilitates DST to excel in zero-shot learning scenarios.

Problem

• Addressing spatial heterogeneity challenges in road network representation learning
• Capturing temporal dynamics of road networks through self-supervised learning
• Overcoming neighborhood smoothing limitations in graph neural networks

Innovation

• Mix-hop transition matrix for dynamic road relations
• Hypergraph contrastive learning for long-range spatial relations
• Causal Transformer with traffic mode regularization for temporal prediction