  Traffic prediction is a crucial topic because of its broad scope of
applications in the transportation domain. Recently, various studies have
achieved promising results. However, most studies assume the prediction
locations have complete or at least partial historical records and cannot be
extended to non-historical recorded locations. In real-life scenarios, the
deployment of sensors could be limited due to budget limitations and
installation availability, which makes most current models not applicable.
Though few pieces of literature tried to impute traffic states at the missing
locations, these methods need the data simultaneously observed at the locations
with sensors, making them not applicable to prediction tasks. Another drawback
is the lack of measurement of uncertainty in prediction, making prior works
unsuitable for risk-sensitive tasks or involving decision-making. To fill the
gap, inspired by the previous inductive graph neural network, this work
proposed an uncertainty-aware framework with the ability to 1) extend
prediction to missing locations with no historical records and significantly
extend spatial coverage of prediction locations while reducing deployment of
sensors and 2) generate probabilistic prediction with uncertainty
quantification to help the management of risk and decision making in the
down-stream tasks. Through extensive experiments on real-life datasets, the
result shows our method achieved promising results on prediction tasks, and the
uncertainty quantification gives consistent results which highly correlated
with the locations with and without historical data. We also show that our
model could help support sensor deployment tasks in the transportation field to
achieve higher accuracy with a limited sensor deployment budget.
