Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting

By Zheng Fang, Qingqing Long, Guojie Song, Kunqing Xie. Knowledge Discovery and Data Mining, 2021

738 citations recorded by the source corpus.

Spatial-temporal forecasting has attracted tremendous attention in a wide range of applications, and traffic flow prediction is a canonical and typical example. The complex and long-range spatial-temporal correlations of traffic flow bring it to a most intractable challenge. Existing works typically utilize shallow graph convolution networks (GNNs) and temporal extracting modules to model spatial and temporal dependencies respectively. However, the representation ability of such models is limited due to: (1) shallow GNNs are incapable to capture long-range spatial correlations, (2) only spatial connections are considered and a mass of semantic connections are ignored, which are of great importance for a comprehensive understanding of traffic networks. To this end, we propose Spatial-Temporal Graph Ordinary Differential Equation Networks (STGODE).1 Specifically, we capture spatial-temporal dynamics through a tensor-based ordinary differential equation (ODE), as a result, deeper networks can be constructed and spatial-temporal features are utilized synchronously. To understand the network more comprehensively, semantical adjacency matrix is considered in our model, and a well-design temporal dialated convolution structure is used to capture long term temporal dependencies. We evaluate our model on multiple real-world traffic datasets and superior performance is achieved over state-of-the-art baselines.

Subfields: Building and Construction, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4b882905c9602d25e28b59dd825d62e19d5ed9a7

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Citations in the corpus

Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey (2023, methodology): “To achieve a joint continuous modeling for spatio-temporal dependencies, MTGODE [208] introduced the integration of Neural-ODE with graph convolution operators and temporal convolution operators to enable continuous spatio-temporal encoding.”
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, methodology): “General pipeline for time series analysis using graph neural networks. factorized STGNN model architectures, the temporal processing isperformedeitherbeforeorafterthespatialprocessing,whether inadiscrete(e.g.,STGCN[54])orcontinuousmanner(e.g., STGODE[74]).”
Graph Neural Controlled Differential Equations for Traffic Forecasting (2021, methodology): “Z-GCNETs (Chen, Segovia-Dominguez, and Gel 2021) integrates the new time-aware zigzag topological layer into time-conditioned GCNs. STGODE (Fang et al. 2021) captures spatial-temporal dynamics through a tensor-based ODE.”
DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting (2022, methodology): “, 2020) that included local spatial-temporal subgraph modules; (7) STFGNN (Li & Zhu, 2021) that used a spatialtemporal fusion graph to complement the spatial correlation; (8) STGODE (Fang et al., 2021) that applied continous graph neural network to traffic prediction in multivariate time series fore”
PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction (2023, background): “…structure of traffic data (Li et al. 2018; Yu, Yin, and Zhu 2018), and thus GNN-based methods have been widely explored in traffic prediction (Wu et al. 2019; Song et al. 2020; Wu et al. 2020; Bai et al. 2020; Chen et al. 2020; Ye et al. 2021; Li and Zhu 2021; Fang et al. 2021; Choi et al. 2022).”