Uncertainty Quantification for Image-based Traffic Prediction across
  Cities
Alexander Timans Nina Wiedemann Nishant Kumar Ye Hong Martin Raubal
Despite the strong predictive performance of deep learning models for traffic
prediction, their widespread deployment in real-world intelligent
transportation systems has been restrained by a lack of interpretability.
Uncertainty quantification (UQ) methods provide an approach to induce
probabilistic reasoning, improve decision-making and enhance model deployment
potential. To gain a comprehensive picture of the usefulness of existing UQ
methods for traffic prediction and the relation between obtained uncertainties
and city-wide traffic dynamics, we investigate their application to a
large-scale image-based traffic dataset spanning multiple cities and time
periods. We compare two epistemic and two aleatoric UQ methods on both temporal
and spatio-temporal transfer tasks, and find that meaningful uncertainty
estimates can be recovered. We further demonstrate how uncertainty estimates
can be employed for unsupervised outlier detection on changes in city traffic
dynamics. We find that our approach can capture both temporal and spatial
effects on traffic behaviour in a representative case study for the city of
Moscow. Our work presents a further step towards boosting uncertainty awareness
in traffic prediction tasks, and aims to highlight the value contribution of UQ
methods to a better understanding of city traffic dynamics.