  Accurate long series forecasting of traffic information is critical for the
development of intelligent traffic systems. We may benefit from the rapid
growth of neural network analysis technology to better understand the
underlying functioning patterns of traffic networks as a result of this
progress. Due to the fact that traffic data and facility utilization
circumstances are sequentially dependent on past and present situations,
several related neural network techniques based on temporal dependency
extraction models have been developed to solve the problem. The complicated
topological road structure, on the other hand, amplifies the effect of spatial
interdependence, which cannot be captured by pure temporal extraction
approaches. Additionally, the typical Deep Recurrent Neural Network (RNN)
topology has a constraint on global information extraction, which is required
for comprehensive long-term prediction. This study proposes a new
spatial-temporal neural network architecture, called Spatial-Temporal
Graph-Informer (STGIN), to handle the long-term traffic parameters forecasting
issue by merging the Informer and Graph Attention Network (GAT) layers for
spatial and temporal relationships extraction. The attention mechanism
potentially guarantees long-term prediction performance without significant
information loss from distant inputs. On two real-world traffic datasets with
varying horizons, experimental findings validate the long sequence prediction
abilities, and further interpretation is provided.
