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.

翻译：交通预测因其在交通领域的广泛应用而成为一个关键课题。近年来，众多研究取得了令人瞩目的成果。然而，大多数研究假设预测位置拥有完整或至少部分历史记录，无法推广到无历史记录的位置。在现实场景中，由于预算限制和安装可行性，传感器的部署可能受限，这使得当前大多数模型不再适用。尽管少数文献尝试对缺失位置的交通状态进行补全，但这些方法需要同时在有传感器的位置观测数据，使其无法适用于预测任务。另一个不足是预测中缺乏不确定性度量，导致先前的工作不适用于风险敏感任务或涉及决策的场景。为填补这一空白，受先前归纳图神经网络的启发，本研究提出了一种不确定性感知框架，具备以下能力：1）将预测扩展到无历史记录的缺失位置，显著扩大预测位置的空间覆盖范围，同时减少传感器部署；2）生成带有不确定性量化的概率预测，以帮助下游任务中的风险管理和决策。通过在真实数据集上的广泛实验，结果表明，我们的方法在预测任务上取得了令人满意的结果，且不确定性量化结果与有无历史数据的位置高度相关且一致。我们还展示了模型如何帮助支持交通领域的传感器部署任务，在有限的传感器部署预算下实现更高的精度。