Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

By Zhiyong Cui, Kristian C. Henrickson, Ruimin Ke, Yinhai Wang. IEEE transactions on intelligent transportation systems (Print), 2018

905 citations recorded by the source corpus.

Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between the proposed traffic graph convolution and the spectral graph convolution is also discussed. An L1-norm on graph convolution weights and an L2-norm on graph convolution features are added to the model’s loss function to enhance the interpretability of the proposed model. Experimental results show that the proposed model outperforms baseline methods on two real-world traffic state datasets. The visualization of the graph convolution weights indicates that the proposed framework can recognize the most influential road segments in real-world traffic networks.

Subfields: Building and Construction, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/83b44a37ddf121e660c873e207c2909b79f23469

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Citations in the corpus

BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation (2021, background): “Graph neural networks (GNNs) have received extensive attention from researchers due to their excellent performance on various graph learning tasks such as social analysis [24, 17, 29], drug discovery [12, 25], traffic forecasting [18, 3, 6], recommendation system [38, 32] and computer vision [39, 4]”
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction (2020, background): “From the sequence prediction perspective, temporal graph RNNs allow learning spatio-temporal relationship in graph sequences [8,17].”
A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions (2024, methodology): “GCNs have been applied to solve many problems, for example, image classification [35], traffic forecasting [36], recommendation systems [17], scene graph generation [37], and visual question answering [38].”
Graph Neural Network for Traffic Forecasting: A Survey (2021, background): “Dataset Name Relevant Studies METR-LA [3], [4], [22], [25], [26], [27], [35], [38], [43], [44], [55], [57], [71], [72], [80], [105], [110], [117], [125], [129], [132], [137], [138], [141], [142], [143], [144], [145], [146], [148], [149], [153], [155], [159], [160], [172] PeMS all [28], [147] PeMS-BA”
Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, methodology): “In other words, two data points might be spatially close in Euclidean space but interact independently when considering traffic operations [4] in a transportation network based on the network connectivity/proximity.”
Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values (2020, background): “There are many studies forecasting traﬃc from other perspectives, like learning traﬃc as images Ma et al. (2017) or graphs Cui et al. (2019) by combining convolutional based neural networks.”
Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution (2021, methodology): “There are also some pre-defined graphs calculated based on other geographical attributes such as Point of Interest (POI [7]), free-flow reachability [6], and so on.”
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting (2020, background): “Similarly, Cui et al. (2019) defined another graph convolutional operator, incorporating the adjacency matrix and a free-flow reachable matrix to capture localized spatial features.”