Sample and Aggregate Voronoi Neighborhood Weighted Graph Neural Network (SAGE-Voronoi) and Its Capability for City-Sized Vehicle Traffic Time Series Prediction

The application of graph convolutional neural networks for traffic prediction is a standard procedure; however, this approach is rarely used under the assumption that the exact city plan is unknown and the prediction area is a city-sized region. This paper fills this gap by proposing and evaluating the Sample and Aggregate-Voronoi method (SAGE-Voronoi), which utilizes the novel concept of Voronoi Neighborhood Weighted Graph-based convolutional networks to predict car traffic in cities. It demonstrates the usefulness of this method for short-term predictions using real sensor data from the moderate-sized town of Darmstadt. The results obtained are compared with those of other neural network algorithms, namely pure Long Short-Term Memory, SAGE, Diffusion Convolutional Gated Recurrent Unit (DCGRU), and Spatio-Temporal Graph Convolutional Neural Network (STGCN). SAGE-Voronoi obtained significantly better results than the state-of-the-art approaches. The SAGE-Voronoi graph neural network enables the reliable prediction of varying car traffic among network nodes. The proposed approach is not limited to spatiotemporal traffic data and can be utilized in other similar domains. The source code and dataset used in our experiments are available for download, enabling full reproducibility of the results.