GMAN: A Graph Multi-Attention Network for Traffic Prediction

TL;DR

This paper introduces GMAN, a graph multi-attention network that models spatio-temporal factors for accurate long-term traffic prediction, outperforming existing methods on real-world datasets.

Contribution

The paper proposes a novel encoder-decoder architecture with spatio-temporal attention blocks and a transform attention layer to improve traffic prediction accuracy.

Findings

GMAN outperforms state-of-the-art methods by up to 4% in MAE for 1-hour ahead predictions.

The model effectively captures spatio-temporal dependencies in traffic data.

Experimental results validate GMAN's superiority on real-world traffic datasets.

Abstract

Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input traffic features and the decoder predicts the output sequence. Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder. The transform attention mechanism models the direct relationships between historical and future time steps that helps to alleviate the error propagation problem among prediction time steps. Experimental results on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure. The source code is available at https://github.com/zhengchuanpan/GMAN.