Asymmetric Long-Term Graph Multi-Attention Network for Traffic Speed Prediction

Abstract

Traffic speed prediction is essential for efficient traffic operation and management by distributing demand concentration in time and space. To make an accurate prediction, it is required to consider spatio-temporal characteristics of the traffic evolution. Recently, deep learning-based approaches, especially Graph Neural Network (GNN) has been widely adopted to reflect the stated characteristics. However, existing GNN models mainly used for short-term prediction, whereas long-term traffic prediction is more useful by enabling earlier and efficient decisions of traffic management as well as individual travels. In this study, we propose Asymmetric Long-Term Graph Multi-Attention Network (ALT-GMAN) algorithm, an extension of the GMAN. ALT-GMAN can predict short and long-term traffic speed by considering asymmetric characteristics of forward and backward waves observed in real roadways. ALT-GMAN is tested with six months highway data of PeMS-Bay area, and MAPE for 3-hours and 6-hours prediction is evaluated as 5.53% and 6.05%, respectively. ALT-GMAN outperforms the existing models in short-term speed prediction, and provides a robust performance in long-term prediction problems, too.