Heterogeneous-Scale Multi-Graph Convolutional Network Based on Kernel Density Estimation for Traffic Prediction

Abstract

Traffic forecasting plays a pivotal role in the advancement of intelligent transportation systems, with significant implications for congestion alleviation and optimal route planning. Existing approaches typically focus on capturing the temporal dynamics of traffic states and the spatial dependencies across road networks to improve prediction accuracy. Nevertheless, two noteworthy limitations persist in these approaches: (1) A lack of consideration for the interaction between spatiotemporal features over varying time scales, which impedes the effective utilization of traffic state information for forecasting future conditions. (2) The inherent stochasticity and distributional imbalances in traffic flow, which introduce uncertainty and contribute to overfitting issues in deep learning models. To address these challenges, we propose a novel method, the heterogeneous-scale multi-graph convolution networks based on kernel density estimation (KDE-HSMGCN). This method integrates two core components: the frequency feature layer and the heterogeneous-scale spatiotemporal layers. The frequency feature layer employs a mapping network to learn and equalize traffic flow distributions, mitigating the effects of distribution imbalance and overfitting during model training. The heterogeneous-scale spatiotemporal layers utilize stacked spatiotemporal layers to capture traffic state information across varying time scales. Experimental evaluations on two diverse traffic datasets demonstrate the superior performance of KDE-HSMGCN in medium and long-term forecasting scenarios.