STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow Prediction

TL;DR

STLGRU is a novel lightweight graph-based neural network that effectively models dynamic spatial-temporal traffic data, achieving state-of-the-art accuracy with reduced memory and computational requirements.

Contribution

The paper introduces STLGRU, a memory-efficient, unified model that captures spatial-temporal dependencies without separate components, advancing traffic prediction methods.

Findings

Achieves state-of-the-art accuracy on real-world datasets.

Uses fewer parameters and less memory than existing models.

Demonstrates competitive computational efficiency.

Abstract

Reliable forecasting of traffic flow requires efficient modeling of traffic data. Indeed, different correlations and influences arise in a dynamic traffic network, making modeling a complicated task. Existing literature has proposed many different methods to capture traffic networks' complex underlying spatial-temporal relations. However, given the heterogeneity of traffic data, consistently capturing both spatial and temporal dependencies presents a significant challenge. Also, as more and more sophisticated methods are being proposed, models are increasingly becoming memory-heavy and, thus, unsuitable for low-powered devices. To this end, we propose Spatio-Temporal Lightweight Graph GRU, namely STLGRU, a novel traffic forecasting model for predicting traffic flow accurately. Specifically, our proposed STLGRU can effectively capture dynamic local and global spatial-temporal relations of traffic networks using memory-augmented attention and gating mechanisms in a continuously synchronized manner. Moreover, instead of employing separate temporal and spatial components, we show that our memory module and gated unit can successfully learn the spatial-temporal dependencies with reduced memory usage and fewer parameters. Extensive experimental results on three real-world public traffic datasets demonstrate that our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency. Our code is available at https://github.com/Kishor-Bhaumik/STLGRU