Higher-Order Masked Graph Neural Networks for Traffic Flow Prediction

Abstract

Spatiotemporal forecasting has been attracting tremendous interest in various fields, among which traffic flow prediction is a representative example. Existing methods typically deal with the complex spatial and temporal dependencies in traffic flow through graph neural networks (GNNs) and temporal neural networks (TNNs), respectively. However, these works still fall short due to: 1) deep GNNs have the over-smoothing problem that hinders the handling of higher-order spatial correlations; 2) TNNs have difficulty in extracting the temporal dependencies with different localities. To this end, this paper proposes Higher-Order Masked Graph Neural Networks (HOMGNNs) to model and predict the traffic flow data. Concretely, we design the spatial graph learning layer to adaptively characterize the dependency correlations of different orders, and the higher-order GNN (HOGNN) is further proposed to deal with these correlations. Furthermore, we define and construct a temporal graph to represent the temporal dynamics in traffic data. The masked GNN (MGNN) is further proposed to extract these dynamics based on the temporal graph. To validate the superiority of the proposed HOMGNNs, we conduct extensive experiments on METR-LA and PEMS-BAY datasets. Experimental results demonstrate the remarkable performance of our method compared with 11 state-of-the-art baselines.