A Self-Correction Transformer Network for Traffic Flow Prediction under Dynamic Spatio-Temporal Distributions

Precise and timely traffic flow prediction plays a
critical role in developing intelligent transportation systems and
has attracted considerable attention in recent decades. The traffic
flow has a non-stationary character in both time and space,
when the drift phenomenon appears, the traffic flow undergoes
significant and sudden changes, bringing the challenge to the
prediction. This paper proposed a self-supervised learning-based
adaptive spatiotemporal Self-Correction Transformer traffic flow
prediction Network (SCTNet). SCTNet can feel the drift with
self-supervised learning, compute distribution features of the test
data, obtain the distribution difference signal, feed it into the
model as network correction information, and then adjust the
spatiotemporal dependence of traffic flow adaptively to enhance
prediction accuracy. The self-supervised learning method can
adjust the model quickly and smoothly, and be utilized in
most existing traffic flow prediction models. The experiments
demonstrate that compared to existing models, the proposed
self-supervised learning SCTNet has achieved state-of-the-art
performance and exhibited strong adaptability to the dynamically
changing spatiotemporal distributions of traffic data.