Graph ensemble deep random vector functional link network for traffic forecasting

Abstract

Traffic forecasting is crucial to achieving a smart city as it facilitates public transportation management, autonomous driving, and the resource relocation of the sharing economy. Traffic forecasting belongs to the challenging spatiotemporal forecasting task, which is highly demanding because of the complicated geospatial correlation between traffic nodes, inconsistent and highly non-linear temporal patterns due to various events, and sporadic traffic accidents. Previous graph neural network (GNN) models built for transportation forecasting feature the sophisticated structure and heavy computation cost as they combine the deep neural network and graph machine learning to capture the spatiotemporal dynamics for the whole transportation network. However, it may be more practical for practitioners to perform node-wise forecasting for specific nodes of interest rather than network-wise forecasting. To mitigate the gaps mentioned above, we propose a novel graph ensemble deep random vector functional link network (GEdRVFL) to forecast the future traffic volume by combining the well-performing ensemble deep random vector functional link (EdRVFL) with the graph convolution layer for a specific node and realize the node-wise traffic forecasting. After a comprehensive comparison with the state-of-the-art models, our model beats the others in four out of five cases measured by mean absolute scaled error.