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.

翻译：尽管深度学习模型在交通预测中展现出强大的预测性能，但其在现实智能交通系统中的广泛应用仍因缺乏可解释性而受到限制。不确定性量化（UQ）方法提供了一种途径，能够引入概率推理、改进决策并增强模型部署的潜力。为了全面了解现有UQ方法在交通预测中的有效性以及所获得的不确定性与城市交通动态之间的关系，我们研究了这些方法在跨越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。我们在时间域和时空迁移任务上比较了两种认知不确定性和两种偶然不确定性量化方法，发现可以恢复出有意义的不确定性估计。我们进一步展示了如何利用不确定性估计对城市交通动态的变化进行无监督异常检测。在以莫斯科市为案例的代表性研究中，我们发现该方法能够捕捉交通行为在时间和空间上的影响。我们的工作朝着增强交通预测任务中的不确定性意识迈出了新的一步，并旨在突出UQ方法对更好理解城市交通动态的价值贡献。