Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

By Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu. 2017

4,347 citations recorded by the source corpus.

Subfields: Graph Learning, Time-Series & Forecasting

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Citations in the corpus

Relational inductive biases, deep learning, and graph networks (2018, background): “…Hamaguchi et al., 2017), to predict the chemical properties of molecules (Duvenaud et al., 2015; Gilmer et al., 2017), to predict traﬃc on roads (Li et al., 2017; Cui et al., 2018), to classify and segment images and videos (Wang et al., 2018c; Hu et al., 2017) and 3D meshes and point clouds…”
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting (2022, methodology): “, use the past twelve time steps (one hour) to predict the future twelve time steps [20, 29, 35, 36, 41].”
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding (2020, background): “Examples include social networks [14, 29], physical systems [2, 10], traffic networks [18, 34], citation networks [1, 14, 16], recommender systems [26, 35], knowledge graphs [3, 24], and so on.”
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey (2023, methodology): “net/ [18], [26], [118]–[122] NYC taxi https://www1.”
Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting (2020, methodology): “For example, Deep CNNs based DeepST [38], ST-ResNet [4], AttConvLSTM [39], DMVST-Net [40], and GCNs based DCRNN [18], STGCN [23] and G-CNN [21], have been proved to achieve the state-of-art performance.”
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks (2020, methodology): “For spatial-temporal graph neural networks, spatial dependencies among nodes are captured by graph convolutions, while temporal dependencies among historical states are preserved by recurrent neural networks [13, 17] or 1D convolutions [22, 23].”
Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction (2021, methodology): “The GCGRU network [47] is an improved version of GRU based on GCN and has been used in trafﬁc prediction [46].”
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction (2021, background): “This is because, on the one hand, Autoformer focuses on modeling seasonal patterns and conducts self-attention at the sub-series level (instead of the raw data), which is much better in extracting long-term temporal patterns than vanilla Transformer-based methods.”
Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics (2020, background): “Modeling real world phenomena, such as climate observations, trafﬁc ﬂow, physics and chemistry simulation (Li et al., 2018; Geng et al., 2019; Long et al., 2018; de Bezenac et al., 2018; Sanchez-Gonzalez et al., 2018; Gilmer et al., 2017), is important but extremely challenging.”
Graph Neural Network for Traffic Forecasting: A Survey (2021, methodology): “For example, the road network is naturally a graph, with the road intersection or segment as the node and the road connection as the edge, and with the graph as the input, several GNN-based models have achieved the outstanding performance than previous approaches in tasks including road traffic flow”
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting (2022, methodology): “from transportation and energy to economics [5, 10, 19], and has remained an enduring research topic in both academia and industry.”
Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting (2021, methodology): “Methods like DCRNN and ASTGCN highly rely on predefined graph which may not capture crucial dependencies between nodes leading to a worse performance.”
Graph Neural Controlled Differential Equations for Traffic Forecasting (2021, neutral): “DCRNN (Li et al. 2018) combines graph convolution with recurrent neural networks in an encoder-decoder manner.”
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting (2023, background): “Another category of works use graph neural networks (GNNs) to capture the cross-dimension dependency explicitly for forecasting (Li et al., 2018; Yu et al., 2018; Cao et al., 2020; Wu et al., 2020).”
DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting (2022, methodology): “, 2018) which claimed to be effective in learning local and global temporal relations; (3) DCRNN (Li et al., 2017) that integrated graph convolution into a gated recurrent unit; (4) STGCN (Yu et al.”