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Adaptive graph generation based on generalized pagerank graph neural network for traffic flow forecasting

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Abstract

Traffic flow forecasting is a typical multivariate time series problem that has applications in intelligent transportation systems. It requires the modeling of complicated spatial-temporal dependencies and essential uncertainty regarding a road network and traffic conditions. Recently, some studies have improved their models without prespecified graphs by constructing adaptive matrices or learnable node embedding dictionaries; however, they omitted the semantic correlations among distant regions. In this paper, we propose an adaptive generalized PageRank graph neural network (AGP-GNN) for traffic flow forecasting, which jointly models spatial, temporal, and semantic correlations to adaptively generate hidden graph structures. Specifically, the AGP-GNN mainly includes two key components: 1) an adaptive generalized PageRank (AGP) layer, which dynamically assigns different edge weights to reflect the different correlations between the pairwise nodes; and 2) a relative position-based temporal attention (RPTA) layer, which models the complex correlations among different time steps. Moreover, we design a distance and temporal encoding (DTE) approach to incorporate geographic and temporal information. Experimental results obtained on two real-world datasets demonstrate the effectiveness of the AGP-GNN.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the agp-gnn (public github repository), https://github.com/guoxiaoyuatbjtu/agp-gnn.

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Acknowledgements

The authors would like to thank the National Natural Science Foundation of China (61876018, 61906014, and 61876017) for their support in this research.

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Authors and Affiliations

  1. School of Software Engineering, Beijing Jiaotong University, Shangyuancun, Beijing, 100044, China

    Xiaoyu Guo, Xiangyuan Kong, Weiwei Xing, Xiang Wei, Jian Zhang & Wei Lu

  2. China North Vehicle Research Institute, Beijing, 100072, China

    Xiangyuan Kong

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Correspondence to Weiwei Xing.

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Guo, X., Kong, X., Xing, W. et al. Adaptive graph generation based on generalized pagerank graph neural network for traffic flow forecasting. Appl Intell 53, 30971–30986 (2023). https://doi.org/10.1007/s10489-023-05137-8

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