Uncertainty Quantification for Image-based Traffic Prediction across Cities

TL;DR

This paper evaluates uncertainty quantification methods for image-based traffic prediction across multiple cities, demonstrating their ability to improve interpretability, detect outliers, and understand traffic dynamics.

Contribution

It systematically compares UQ methods on large-scale traffic data, revealing their effectiveness in capturing uncertainties and aiding in traffic analysis across different urban environments.

Findings

Uncertainty estimates can be meaningfully recovered for traffic prediction.

UQ methods enable unsupervised outlier detection in traffic dynamics.

The approach captures temporal and spatial traffic effects in Moscow.

Abstract

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.