Spatiotemporal Adaptive Fusion Graph Network for Short-Term Traffic Flow Forecasting

Shumin Yang, Huaying Li, Yu Luo, Junchao Li, Youyi Song and Teng Zhou
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Shumin Yang: Department of Computer Science, Shantou University, Shantou 515000, China
Huaying Li: Department of Computer Science, Shantou University, Shantou 515000, China
Yu Luo: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
Junchao Li: Mechanical Engineering College, Xi’an Shiyou University, Xi’an 710312, China
Youyi Song: Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China
Teng Zhou: Department of Computer Science, Shantou University, Shantou 515000, China

Mathematics, 2022, vol. 10, issue 9, 1-12

Abstract: Traffic flow forecasting is challenging for us to analyze intricate spatial–temporal dependencies and obtain incomplete information of spatial–temporal connection. Existing frameworks mostly construct spatial and temporal modeling based on a fixed graph structure and given time series. However, a fixed adjacency matrix is limited to learn effective spatial–temporal correlations of the network because it represents incomplete information for missing genuine relation. To solve the difficulty, we design a novel spatial–temporal adaptive fusion graph network (STFAGN) for traffic prediction. First, our model combines fusion convolution layers with a novel adaptive dependency matrix by end-to-end training to capture the hidden spatial-temporal dependency on the data to complete incomplete information. Second, STFAGN could, in parallel, acquire hidden spatial–temporal dependencies by a fusion operation and temporal trend by fast-DTW. Meanwhile, we use ReZero connection as a simple change of deep residual networks to facilitate deep signal propagation and faster converge. Lastly, we conduct comparative experiments on two public traffic network datasets, whose results demonstrate the superiority of our algorithm compared to state-of-the-art baseline types. Ablation experiments also prove the rationality of the framework of STFAGN.

Keywords: intelligent transportation system; traffic flow modeling; time series analysis; deep learning; noise-immune learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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