  Many traffic prediction applications rely on uncertainty estimates instead of
the mean prediction. Statistical traffic prediction literature has a complete
subfield devoted to uncertainty modelling, but recent deep learning traffic
prediction models either lack this feature or make specific assumptions that
restrict its practicality. We propose Quantile Graph Wavenet, a Spatio-Temporal
neural network that is trained to estimate a density given the measurements of
previous timesteps, conditioned on a quantile. Our method of density estimation
is fully parameterised by our neural network and does not use a likelihood
approximation internally. The quantile loss function is asymmetric and this
makes it possible to model skewed densities. This approach produces uncertainty
estimates without the need to sample during inference, such as in Monte Carlo
Dropout, which makes our method also efficient.
