  The high dynamics and heterogeneous interactions in the complicated urban
systems have raised the issue of uncertainty quantification in spatiotemporal
human mobility, to support critical decision-makings in risk-aware web
applications such as urban event prediction where fluctuations are of
significant interests. Given the fact that uncertainty quantifies the potential
variations around prediction results, traditional learning schemes always lack
uncertainty labels, and conventional uncertainty quantification approaches
mostly rely upon statistical estimations with Bayesian Neural Networks or
ensemble methods. However, they have never involved any spatiotemporal
evolution of uncertainties under various contexts, and also have kept suffering
from the poor efficiency of statistical uncertainty estimation while training
models with multiple times. To provide high-quality uncertainty quantification
for spatiotemporal forecasting, we propose an uncertainty learning mechanism to
simultaneously estimate internal data quality and quantify external uncertainty
regarding various contextual interactions. To address the issue of lacking
labels of uncertainty, we propose a hierarchical data turbulence scheme where
we can actively inject controllable uncertainty for guidance, and hence provide
insights to both uncertainty quantification and weak supervised learning.
Finally, we re-calibrate and boost the prediction performance by devising a
gated-based bridge to adaptively leverage the learned uncertainty into
predictions. Extensive experiments on three real-world spatiotemporal mobility
sets have corroborated the superiority of our proposed model in terms of both
forecasting and uncertainty quantification.
