A multi‐perspective fusion model for operating speed prediction on highways using knowledge‐enhanced graph neural networks

Abstract

AbstractThis study proposes a multi‐perspective fusion model for operating speed prediction based on knowledge‐enhanced graph neural networks, named RoadGNN‐S. By utilizing message passing and multi‐head self‐attention mechanisms, RoadGNN‐S can effectively capture the coupling impacts of multi‐perspective alignment elements (i.e., two‐dimensional design, 2.5‐dimensional driving, and three‐dimensional spatial perspectives). The results of driving simulation data show that root mean squared error, mean absolute error, mean absolute percentage error, and R‐squared values of RoadGNN‐S are superior to those of other classic deep learning algorithms. Then, prior knowledge (i.e., highway geometry supply, driver expectations, and vehicle dynamics) is introduced into RoadGNN‐S, and the models’ prediction accuracy and transferability are verified by field observation experiments. Compared to the above data‐driven models, knowledge‐enhanced RoadGNN‐S effectively avoids the fundamental errors, improving the R‐squared value in predicting passenger cars’ and trucks’ operating speed by 7.9% and 10.7%, respectively. The findings of this study facilitate the intelligent highway geometric design with multi‐perspective fusion and knowledge enhancement techniques.