· 2018
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
- 3,333citations
- 2018year
Short summary
A novel Spatio-Temporal Graph Convolutional Network (STGCN) framework is introduced for traffic forecasting, effectively capturing complex spatio-temporal correlations.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces Spatio-Temporal Graph Convolutional Networks (STGCN) for traffic forecasting.
- Models traffic flow as a graph problem using a complete convolutional structure.
- Achieves faster training speeds and requires fewer parameters than traditional methods.
- Effectively captures comprehensive spatio-temporal correlations.
- Outperforms state-of-the-art baselines on real-world traffic datasets.
AI-generated from the title and abstract; the full text is not read.
Abstract
Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.
The authors' abstract, as published at the source., 2018 · DOI ↗
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Field: Building and Construction
Building and ConstructionEngineering