Sparse Big Data for Vehicular Network Traffic Flow Estimation: A Machine Learning Approach

Traffic flow estimation (TFE) plays an important role in intelligent transportation systems (ITS). Considering the prohibitive overhead of collecting and processing massive data in vehicular networks, it is more practical to use the sparse vehicular big data. In this paper, we focus on accurately estimating the urban traffic flow of vehicular networks only using a small portion of vehicular data. A new spatiotemporal machine learning model, named Graph Sampling and Aggregate Transformer (GSAT), is developed to improve the estimation accuracy by leveraging the inner correlation of traffic data. Specifically, the GSAT uses the graph sample and aggregate (GraphSAGE) model, a variety of graph neural network (GNN), to aggregate the spatial correlation and applies the Transformer model to capture the temporal correlation. We evaluate GSAT at multiple sparsity on the real world dataset of vehicular network, and it is demonstrated that GSAT can achieve accurate estimation with sparse big data.