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An Optimized Temporal-Spatial Gated Graph Convolution Network for Traffic Forecasting
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AI
DOI：10.1109/MITS.2019.2962138.png)
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Abstract
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Traffic forecasting is a challenging problem because of the irregular and complex road network in space and the dynamic and non-stationary traffic flow in time. To solve this problem, the recently proposed temporal graph convolution models abstracted the spatial and temporal features of the traffic system and obtained considerable improvement. However, most of the current methods use empirical graphs to represent the road network, which don't fully extract the spatial and temporal features. This paper proposes an Optimized Temporal-Spatial Gated Graph Convolution Network (OTSGGCN) for traffic forecasting, in which the spatial-temporal traffic feature is captured by an innovative graph convolution network with the graph constructed in a data-driven way. The experiments on two real-world traffic datasets show that the proposed method outperforms the state of the art traffic forecasting methods.
Keywords:
Forecasting
Convolution
Logic gates
Roads
Neural networks
Feature extraction
Matrix decomposition
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