Small Graph Is All You Need: DeepStateGNN for Scalable Traffic Forecasting

TL;DR

DeepStateGNN introduces a scalable, adaptive graph neural network that clusters traffic sensors into higher-level nodes for improved forecasting and reconstruction in large-scale traffic networks.

Contribution

The paper presents DeepStateGNN, a novel GNN model that clusters sensors into dynamic, higher-level nodes, enhancing scalability and accuracy in traffic data analysis.

Findings

Outperforms existing methods in accuracy for traffic forecasting and reconstruction.

Offers faster training and better scalability for large sensor networks.

Demonstrates effectiveness across various similarity-based clustering criteria.

Abstract

We propose a novel Graph Neural Network (GNN) model, named DeepStateGNN, for analyzing traffic data, demonstrating its efficacy in two critical tasks: forecasting and reconstruction. Unlike typical GNN methods that treat each traffic sensor as an individual graph node, DeepStateGNN clusters sensors into higher-level graph nodes, dubbed Deep State Nodes, based on various similarity criteria, resulting in a fixed number of nodes in a Deep State graph. The term "Deep State" nodes is a play on words, referencing hidden networks of power that, like these nodes, secretly govern traffic independently of visible sensors. These Deep State Nodes are defined by several similarity factors, including spatial proximity (e.g., sensors located nearby in the road network), functional similarity (e.g., sensors on similar types of freeways), and behavioral similarity under specific conditions (e.g., traffic behavior during rain). This clustering approach allows for dynamic and adaptive node grouping, as sensors can belong to multiple clusters and clusters may evolve over time. Our experimental results show that DeepStateGNN offers superior scalability and faster training, while also delivering more accurate results than competitors. It effectively handles large-scale sensor networks, outperforming other methods in both traffic forecasting and reconstruction accuracy.