STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems. (arXiv:2202.06727v2 [cs.LG] UPDATED)

Short-term passenger flow prediction is an important but challenging task for
better managing urban rail transit (URT) systems. Some emerging deep learning
models provide good insights to improve short-term prediction accuracy.
However, there exist many complex spatiotemporal dependencies in URT systems.
Most previous methods only consider the absolute error between ground truth and
predictions as the optimization objective, which fails to account for spatial
and temporal constraints on the predictions. Furthermore, a large number of
existing prediction models introduce complex …

arxiv flow gan generative adversarial networks graph networks prediction rail systems transit