Multiadaptive Spatiotemporal Flow Graph Neural Network for Traffic Speed Forecasting

Abstract

Traffic speed forecasting plays an important role in intelligent traffic monitoring systems. Existing methods mostly predefine a fixed adjacency matrix to capture the spatial correlation between sensors in a traffic network. However, there are multiple hidden spatial correlations between sensors. A single fixed adjacency matrix cannot adaptively capture multiple spatial correlations. To overcome this limitation, we proposed a novel multiadaptive spatiotemporal flow graph neural network (MAF-GNN) for traffic speed forecasting. Specifically, MAF-GNN mainly consists of a multiadaptive adjacency matrix mechanism and a spatiotemporal flow mechanism. The multiadaptive adjacency matrix mechanism was proposed to adaptively capture multiple hidden spatial correlations between sensors. The spatiotemporal flow mechanism was proposed to further enhance the capture of temporal and spatial correlations. The experimental results on two real-world traffic datasets, METR-LA and PeMS-Bay, demonstrated the superiority of MAF-GNN. MAF-GNN outperformed baseline models in 1-h ahead forecasting.