2D Spatiotemporal Hypergraph Convolution Network for Dynamic OD Traffic Flow Prediction

Abstract

Predicting origin-destination (OD) flow presents a significant challenge in intelligent transportation due to the intricate dynamic correlations between starting points and destinations. Although existing OD prediction methodologies leveraging graph neural networks have demonstrated commendable performance, they often struggle to address the complexities inherent in two-sided correlations. To address this gap, this paper introduces a novel approach, the 2D spatiotemporal hypergraph convolution network (2D-HGCN), designed specifically for forecasting OD traffic flow. Our proposed model employs a two-stage architecture. Initially, temporal characteristics of traffic flow between OD pairs are captured using a 1D convolution neural network (1D-CNNs). Subsequently, a 2D hypergraph convolutional network is introduced to uncover spatial correlations in OD flow patterns. The unique aspect of our 2D-HGCN lies in its dynamic hypergraph, which evolves over time, enabling the model to adaptively learn changing spatial dependencies. Experimental evaluation conducted on real-world datasets highlights the efficiency of our suggested model for predicting OD flows. Our results demonstrate a promising predictive performance, showcasing the ability of the 2D-HGCN to effectively capture the intricate dynamics of OD traffic flow.