T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
TLDR
In this article, a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutionsal network and the gated recurrent unit (GRU), is proposed.Abstract:
Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an “open” scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://www.github.com/lehaifeng/T-GCN .read more
Citations
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Proceedings ArticleDOI
Traffic Flow Prediction via Spatial Temporal Graph Neural Network
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Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting
TL;DR: This work proposes a novel deep learning architecture called Traffic Transformer to capture the continuity and periodicity of time series and to model spatial dependency, taking inspiration from Google’s Transformer framework for machine translation.
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Urban flow prediction from spatiotemporal data using machine learning: A survey
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