@article{ref1,
title="Research on traffic flow forecasting based on dynamic spatial-temporal transformer",
journal="Transportation research record",
year="2024",
author="Zhang, Hong and Wang, Hongyan and Zhang, Xijun and Gong, Lei",
volume="2678",
number="7",
pages="301-313",
abstract="Accurate traffic flow forecasting is crucial for urban traffic control and route planning. Aiming at the difficulty in capturing dynamic spatio-temporal complexity of traffic flow, a dynamic spatio-temporal transformer (DST-Trans) model capable of modeling dynamic correlation of traffic flow is proposed, which consists of gated temporal convolutional network (GTCN), graph convolutional network (GCN), and spatio-temporal transformer (ST-TF). GTCN and GCN are utilized to capture the temporal and spatial characteristics of traffic flow, respectively. ST-TF includes a temporal transformer using temporal gated convolution and temporal multi-head self-attention to capture short-long term temporal features, and spatial transformer using spatial gated graph convolution and spatial multi-head self-attention to capture local-global dynamic spatial features. In addition, to take full advantage of the dynamic and static associations of road networks, multi-graph models of road relationship graph, similarity graph, and adaptive dynamic graph with SGGC are constructed. Experimental results show that the DST-Trans model in this paper shows good prediction performance in short-term (15 min), medium-term (30 min), and long-term (60 min) prediction, outperforming existing state-of-the-art models by up to approximately 7%.<p /> <p>Language: en</p>",
language="en",
issn="0361-1981",
doi="10.1177/03611981231205880",
url="http://dx.doi.org/10.1177/03611981231205880"
}