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.

深度学习模型对交通预测的强大预测性能使其得到广泛应用，但缺乏可解释性限制了其在实际智能交通系统中的部署。通过使用不确定性量化方法，我们调查了这些方法在跨多个城市和时间段的大规模基于图像的交通数据集上的应用，以获得对现有不确定性方法在交通预测中的实用性以及不确定性与城市交通动态之间的关系的全面认识。在在时间和时空转移任务上比较了两种认知不确定性方法和两种他稳不确定性方法后，我们发现可以恢复有意义的不确定性估计。此外，我们进一步演示了如何利用不确定性估计来检测城市交通动态变化中的无监督异常点。通过对莫斯科市的代表性案例研究，我们发现我们的方法能够捕捉到交通行为的时间和空间效应。我们的工作是在交通预测任务中推动不确定性意识的进一步发展，并旨在突出不确定性量化方法对于更好理解城市交通动态的价值贡献。