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Accurate long series forecasting of traffic information is critical for thedevelopment of intelligent traffic systems. We may benefit from the rapidgrowth of neural network analysis technology to better understand theunderlying functioning patterns of traffic networks as a result of thisprogress. Due to the fact that traffic data and facility utilizationcircumstances are sequentially dependent on past and present situations,several related neural network techniques based on temporal dependencyextraction models have been developed to solve the problem. The complicatedtopological road structure, on the other hand, amplifies the effect of spatialinterdependence, which cannot be captured by pure temporal extractionapproaches. Additionally, the typical Deep Recurrent Neural Network (RNN)topology has a constraint on global information extraction, which is requiredfor comprehensive long-term prediction. This study proposes a newspatial-temporal neural network architecture, called Spatial-TemporalGraph-Informer (STGIN), to handle the long-term traffic parameters forecastingissue by merging the Informer and Graph Attention Network (GAT) layers forspatial and temporal relationships extraction. The attention mechanismpotentially guarantees long-term prediction performance without significantinformation loss from distant inputs. On two real-world traffic datasets withvarying horizons, experimental findings validate the long sequence predictionabilities, and further interpretation is provided.

STGIN: A Spatial Temporal Graph-Informer Network for Long Sequence Traffic Speed Forecasting