A Visual Analytics System for Improving Attention-based Traffic Forecasting Models. (arXiv:2208.04350v2 [cs.HC] UPDATED)

With deep learning (DL) outperforming conventional methods for different
tasks, much effort has been devoted to utilizing DL in various domains.
Researchers and developers in the traffic domain have also designed and
improved DL models for forecasting tasks such as estimation of traffic speed
and time of arrival. However, there exist many challenges in analyzing DL
models due to the black-box property of DL models and complexity of traffic
data (i.e., spatio-temporal dependencies). Collaborating with domain experts,
we design a …

analytics arxiv attention forecasting traffic visual analytics