General Semantic Knowledge Infusion for Spatio-Temporal Traffic Forecasting

Knowledge-Enhanced Graph Neural Network for Traffic Flow Prediction

To address the challenges of existing traffic volume prediction methods in effectively integrating heterogeneous data and mining deep spatio-temporal semantic correlations, this paper proposes a prediction method based on a Spatio-Temporal Knowledge Graph (STKG). The approach first constructs a knowledge graph to uniformly model road network topology, dynamic traffic flow, and external influencing factors. Then, a knowledge-enhanced spatio-temporal graph neural network is designed to deeply integrate knowledge semantics with real-time data through a unique fusion mechanism, aiming to capture the complex evolutionary patterns of the traffic system. Experimental results demonstrate that the method significantly enhances prediction accuracy in complex road network and sudden event scenarios, improving semantic association accuracy by 17.4% and reducing the Root Mean Square Error by 15.2%.

Unified Spatio-Temporal Tokens are Bases for Generalizable Traffic Forecasting

Traffic forecasting plays a crucial role in real-world applications such as traffic management and urban planning. Recent studies have mainly focused on spatio-temporal graph neural networks (STGNNs) and attention-based methods, which have shown promising results. Nevertheless, both approaches model spatial information implicitly, which limits their ability to generalize across different traffic networks. In this paper, we propose Spatio-Temporal Unified Network (STUNet), a framework to explicitly encode spatial features into unified representations and integrate them with temporal information effectively. To obtain spatial representations explicitly, we design a spatial tokenizer that segments the adjacency matrix of the relation graph into patches to serve as spatial tokens. Furthermore, to effectively integrate spatial and temporal representations, we introduce query-aggregate attention, which simulates the process of tracing upstream and downstream nodes and aggregating their information, thereby capturing complex spatio-temporal dependencies. Extensive experiments on traffic benchmarks demonstrate that STUNet achieves generalization across different traffic networks with competitive performance. Code is available at https://github.com/JimmyChen6/STUNet.

Traffic Flow Prediction System Based on Spatiotemporal Graph Neural Network

This framework introduces an adaptive graph learning module that dynamically infers meaningful connectivity relationships among traffic sensors—not relying on fixed geographic or distance-based assumptions—but instead leveraging real-time traffic correlations and node-level embeddings, enabling effective modeling of both localized spatial interactions and multi-scale temporal dependencies across varying prediction horizons.

ASTPKEFormer: Adaptive Spatiotemporal Prior Knowledge Embedding-Induced Transformers for Traffic Data Forecasting

Building Urban Traffic Flow Prediction Model Using Spatio-Temporal Graph Convolutional Networks

The proposed position-aware spatio-temporal modeling strategy provides a practical reference for information fusion and dynamic state estimation in large-scale wireless sensing networks and electromagnetic signal-driven monitoring systems, supporting future intelligent perception and communication infrastructures.

Structure-Guided Spatiotemporal Attention Graph Neural Network for Traffic Flow Prediction

Deep spatiotemporal models integrating graph convolutions and attention mechanisms have demonstrated excellent performance in network-level traffic flow prediction, owing to their exceptional ability to capture complex spatiotemporal dependencies. Despite their predictive success, deployment of such models in safety-critical urban systems remains constrained by their inherent lack of transparency. Existing post-hoc diagnostic methods often struggle with spurious correlations and fail to unveil the intrinsic decision-making mechanisms governing traffic dynamics, resulting in suboptimal interpretability and limited operational trustworthiness. To address these challenges, this paper proposes the Structure-Guided Spatiotemporal Attention Graph Neural Network (SGSAN). Departing from traditional architectures that rely on unconstrained adaptive graphs, SGSAN explicitly learns a static Directed Dependency Graph (DDG) to identify the invariant macroscopic propagation paths of traffic states. We further introduce an InfoNCE-based soft-coupling mechanism that anchors the model's dynamic spatiotemporal attention to this structural prior, offering a mechanistic account of the model's decision-making process while ensuring robust forecasting by aligning attention-based reasoning with identified macroscopic dependencies and preventing over-reliance on ephemeral local noise. Furthermore, a decoupled two-stage optimization framework is developed to resolve the fundamental conflict between structural discovery and predictive error minimization. Extensive experiments on multiple real-world datasets demonstrate that SGSAN achieves state-of-the-art predictive accuracy while providing built-in interpretability that organically aligns with the physical logic of traffic networks.