Skip to content
Log in
Open access

Traffic Flow Prediction System Based on Spatiotemporal Graph Neural Network

Zhengxu Luan Huan Wang Miaobowen Wang Yitong Chen Xinxin Chao Xinyue Cao Zhiyong Pan
Jul 2026 · Computers and artificial intelligence · 0 citations · 10 references

TL;DR

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.

Abstract

Urban traffic congestion poses a persistent challenge to sustainable mobility, demanding intelligent forecasting systems capable of capturing both the complex spatial layout of road networks and the dynamic evolution of traffic patterns over time. Traditional statistical and deep learning methods often fail to jointly model these interdependent spatiotemporal characteristics, particularly when road topology is incomplete, outdated, or oversimplified. To address this gap, we propose a novel spatiotemporal graph neural network architecture specifically designed for short-term traffic flow prediction. Our 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. This learned structure is then integrated into a hierarchical encoder that combines spectral graph convolution with dilated causal temporal convolutions, enabling effective modeling of both localized spatial interactions and multi-scale temporal dependencies across varying prediction horizons. A lightweight, shared-weight decoder supports flexible multi-step forecasting without sacrificing accuracy or efficiency. We rigorously evaluate our approach on two large-scale, real-world traffic datasets—PeMSD4 and PeMSD8—covering diverse urban environments and sensor densities. Experimental results demonstrate consistent and statistically significant improvements over state-of-the-art baselines, achieving more than a twelve percent reduction in mean absolute error for fifteen-minute forecasts. Ablation studies confirm the critical role of adaptive graph construction and hierarchical temporal modeling, while visualization of learned graphs reveals interpretable, physically plausible connectivity patterns aligned with actual road infrastructure and traffic behavior. The system shows strong deployment potential, balancing predictive performance with practical considerations such as inference speed and compatibility with edge-computing platforms used in modern traffic management centers.

DOI Semantic Scholar
Read PDF

Similar papers

Preprint Aug 2026

Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computing

A spatiotemporal graph Transformer framework that jointly models spatial interactions and temporal dependencies for traffic forecasting in edge computing and leverages Transformer-based self-attention to learn long-range temporal patterns from historical traffic observations is proposed.

Laha Ale, Letian Lin, Na Cao et al. · 0 citations
Open access 2026

Enhanced Traffic Prediction Using Spatial–Temporal Graph Neural Networks

A Spatial-Temporal Graph Neural Network framework that can learn a combination of spatial and temporal relationships in road networks and changing time-varying patterns in traffic flow is used, which implies that adaptive spatial learning, together with the temporal sequence modeling, can produce much better forecasting stability.

Akarsh Kumar, Aishwarya Sinha, Yadvendra Pratap Singh · 0 citations
Open access Jul 2026

Tensor-evolving graph with temporal separation network for traffic flow forecasting

A Tensor-Evolving Graph with Temporal Separation Network (TEG-TSNet) for traffic flow forecasting is proposed, which constructs a unified spatial prior via graph Laplacian spectral embedding and introduces a time-conditioned structure generation paradigm.

Hongyan Wang, Linlong Chen · 0 citations
Preprint Aug 2026

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.

Xuan He, Can Li, Wanjing Ma · 0 citations
Open access Jul 2026

Recurrent Graph Neural Network Hybrid Model for Spatio-Temporal Traffic Flow Prediction in Intelligent Transportation Systems

TETRA is proposed, a hybrid spatio-temporal traffic forecasting model that integrates Graph Convolutional Networks (GCNs) with Extended Long Short-Term Memory (xLSTM) to capture complex multi-timescale temporal patterns, including congestion propagation and delayed recovery dynamics, which are not well represented by conventional recurrent models.

Norman Bereczki, Vilmos Simon · 0 citations
Open access Aug 2026

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.

J. Sun, Y. Liu, Y. Dou et al. · 0 citations