Traffic Flow Prediction System Based on Spatiotemporal Graph Neural Network
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to jointly model these interdependent spatiotemporal characteristics, particularly when road topology is incomplete, outdated, or oversimplified.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.