  In an intelligent transportation system, the key problem of traffic
forecasting is how to extract periodic temporal dependencies and complex
spatial correlations. Current state-of-the-art methods for predicting traffic
flow are based on graph architectures and sequence learning models, but they do
not fully exploit dynamic spatial-temporal information in the traffic system.
Specifically, the temporal dependencies in the short-range are diluted by
recurrent neural networks. Moreover, local spatial information is also ignored
by existing sequence models, because their convolution operation uses global
average pooling. Besides, accidents may occur during object transition, which
will cause congestion in the real world and further decrease prediction
accuracy. To overcome these challenges, we propose Spatial-Temporal
Conv-sequence Learning (STCL), where a focused temporal block uses
unidirectional convolution to capture short-term periodic temporal dependencies
effectively, and a patial-temporal fusion module is responsible for extracting
dependencies of interactions and decreasing the feature dimensions. Moreover,
as the accidents features have an impact on local traffic congestion, we employ
position encoding to detect anomalies in complex traffic situations. We have
conducted a large number of experiments on real-world tasks and verified the
effectiveness of our proposed method.
