MCAGCN: Multi‐component attention graph convolutional neural network for road travel time prediction

Abstract

With the development of intelligent transportation technology, road travel time prediction has become an important research direction. Owing to the complex periodic dependence and non‐linear features of road travel time series, achieving accurate and effective predictions remains a challenging task. Most existing traffic sequence prediction methods lack modelling of the dynamic correlation between multiple period information, resulting in unsatisfactory prediction results. To address this, a multi‐component attention graph convolutional network (MCAGCN) is proposed for road travel time prediction. First, the spatial‐temporal features of three historical components (hourly, daily and weekly) are modelled individually. A skip attention layer is then used to fuse multi‐scale spatial‐temporal features to enhance the model's feature extraction capabilities. Secondly, a component attention layer is proposed to calculate the correlation between different components using the temporal features of the prediction moment, to achieve dynamic modelling between different period information. The experimental results on the Tianchi, METR‐LA, and PeMS‐BAY datasets, which are real‐world traffic forecasting datasets, demonstrate the superiority of MCAGCN.

Innovative decision making tools using discrete mathematics for stress urinary incontinence treatment

“…Šutić et al described the first solution to address the power flow problem using the Newton–Raphson method, which is a known approach for solving systems of nonlinear equations 37 . Zhao et al used a graph convolution-based method to deal with complex periodic dependencies, nonlinearities, and spatial dependencies within the complex network of urban roads 38 . These examples demonstrate how graph theory can be applied to the analysis of complex networks with nonlinear relationships.…”