ST_AGCNT: Traffic Speed Forecasting Based on Spatial–Temporal Adaptive Graph Convolutional Network with Transformer

Rongjun Cheng (), Mengxia Liu and Yuanzi Xu
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Rongjun Cheng: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
Mengxia Liu: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
Yuanzi Xu: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China

Sustainability, 2025, vol. 17, issue 5, 1-17

Abstract: Traffic speed prediction is difficult because of the complicated dynamic spatiotemporal correlations. Recent studies in spatiotemporal models have achieved impressive outcomes for traffic speed prediction. But many studies use graphs in graph convolutional networks to learn spatial features that are often static. Additionally, effectively modeling long-range temporal features is crucial for prediction accuracy. In order to overcome these challenges, a Spatial–Temporal Adaptive Graph Convolutional Network with Transformer (ST_AGCNT) is designed in this paper. Specifically, an adaptive graph convolution network (AGCN) is designed to extract spatial dependency. An adaptive graph that fuses predefined matrices and learnable matrix is proposed to learn the correlations between nodes. The predefined matrices provide the model with richer prior information, while the learnable matrix can extract the dynamic nature of the nodes. And a temporal transformer (TT) is proposed to extract the long-range temporal dependency. In addition, to learn more information to achieve better results, different historical segments are modeled. Experiments conducted on a real-world traffic dataset confirm the effectiveness of the proposed model when compared to other baseline models. This model demonstrated excellent performance in prediction tasks across different time steps, effectively accomplishing traffic speed forecasting. It provides data support for improving traffic efficiency and reducing resource waste, contributing to the sustainable development of traffic management.

Keywords: adaptive graph; graph convolution network; temporal transformer; traffic speed prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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