Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion

TL;DR

This paper introduces ScaleSTF, a scalable, energy-informed graph neural diffusion model based on Transformer structures, capable of accurately predicting large-scale urban network dynamics with improved efficiency.

Contribution

It proposes a novel, interpretable neural diffusion scheme inspired by physical laws, achieving state-of-the-art performance and scalability in large urban systems.

Findings

State-of-the-art accuracy on urban traffic, solar power, and smart meter datasets

Linear complexity enables scalability to large networks

Energy-informed design improves model interpretability and efficiency

Abstract

Networked urban systems facilitate the flow of people, resources, and services, and are essential for economic and social interactions. These systems often involve complex processes with unknown governing rules, observed by sensor-based time series. To aid decision-making in industrial and engineering contexts, data-driven predictive models are used to forecast spatiotemporal dynamics of urban systems. Current models such as graph neural networks have shown promise but face a trade-off between efficacy and efficiency due to computational demands. Hence, their applications in large-scale networks still require further efforts. This paper addresses this trade-off challenge by drawing inspiration from physical laws to inform essential model designs that align with fundamental principles and avoid architectural redundancy. By understanding both micro- and macro-processes, we present a principled interpretable neural diffusion scheme based on Transformer-like structures whose attention layers are induced by low-dimensional embeddings. The proposed scalable spatiotemporal Transformer (ScaleSTF), with linear complexity, is validated on large-scale urban systems including traffic flow, solar power, and smart meters, showing state-of-the-art performance and remarkable scalability. Our results constitute a fresh perspective on the dynamics prediction in large-scale urban networks.