Traffic prediction is a crucial topic because of its broad scope of
applications in the transportation domain. Recently, various studies have
achieved promising results. However, most studies assume the prediction
locations have complete or at least partial historical records and cannot be
extended to non-historical recorded locations. In real-life scenarios, the
deployment of sensors could be limited due to budget limitations and
installation availability, which makes most current models not applicable.
Though few pieces of literature tried to impute traffic states at the missing
locations, these methods need the data simultaneously observed at the locations
with sensors, making them not applicable to prediction tasks. Another drawback
is the lack of measurement of uncertainty in prediction, making prior works
unsuitable for risk-sensitive tasks or involving decision-making. To fill the
gap, inspired by the previous inductive graph neural network, this work
proposed an uncertainty-aware framework with the ability to 1) extend
prediction to missing locations with no historical records and significantly
extend spatial coverage of prediction locations while reducing deployment of
sensors and 2) generate probabilistic prediction with uncertainty
quantification to help the management of risk and decision making in the
down-stream tasks. Through extensive experiments on real-life datasets, the
result shows our method achieved promising results on prediction tasks, and the
uncertainty quantification gives consistent results which highly correlated
with the locations with and without historical data. We also show that our
model could help support sensor deployment tasks in the transportation field to
achieve higher accuracy with a limited sensor deployment budget.

通过建立一个不确定性感知框架，本研究提出了一种能够推广预测至无历史记录的缺失位置、显著扩展预测位置的空间覆盖程度以及减少传感器部署数量的方法，并生成包含不确定性量化的概率预测，以助于风险管理和决策制定。通过对真实数据集的广泛实验，结果表明我们的方法在预测任务上取得了有希望的结果，并且不确定性量化结果与有和无历史数据的位置高度相关。我们还展示了我们的模型能够在交通领域的传感器部署任务中，以有限的传感器部署预算实现更高的准确性。

不完整数据下的不确定性感知交通预测

Uncertainty-aware Traffic Prediction under Missing Data

Due to the comprehensive nature of this paper, it has been updated and split
into two separate papers: "A Framework For Benchmarking
Class-out-of-distribution Detection And Its Application To ImageNet" and "What
Can We Learn From The Selective Prediction And Uncertainty Estimation
Performance Of 523 Imagenet Classifiers". We recommend reading them instead.
Deep neural networks must be equipped with an uncertainty estimation
mechanism when deployed for risk-sensitive tasks. This paper studies the
relationship between deep architectures and their training regimes with their
corresponding selective prediction and uncertainty estimation performance. We
consider both in-distribution uncertainties and class-out-of-distribution ones.
Moreover, we consider some of the most popular estimation performance metrics
previously proposed including AUROC, ECE, AURC, and coverage for selective
accuracy constraint. We present a novel and comprehensive study of selective
prediction and the uncertainty estimation performance of 484 existing
pretrained deep ImageNet classifiers that are available at popular
repositories. We identify numerous and previously unknown factors that affect
uncertainty estimation and examine the relationships between the different
metrics. We find that distillation-based training regimes consistently yield
better uncertainty estimations than other training schemes such as vanilla
training, pretraining on a larger dataset and adversarial training. We also
provide strong empirical evidence showing that ViT is by far the most superior
architecture in terms of uncertainty estimation performance, judging by any
aspect, in both in-distribution and class-out-of-distribution scenarios.

探讨深度神经网络架构与训练机制与其相应的选择性预测和不确定性估计性能的关系，并在 484 个预训练的深度 Imagenet 分类器中进行了全面的选择性预测和不确定性估计性能研究，发现 ViT 架构在不确定性估计性能方面表现最优。