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项关键课题。近年来，多种研究已取得显著成果。然而，多数研究假设预测位置具有完整或至少部分历史记录，无法扩展至无历史记录的预测点。实际场景中，传感器部署可能因预算限制和安装条件受限，导致现有模型大多不适用。尽管少数文献尝试对缺失位置进行交通状态补全，但这些方法需同时利用有传感器位置的数据，因此不适用于预测任务。另一不足是预测中缺乏不确定性度量，使得先前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，受归纳图神经网络启发，本研究提出了一种不确定性感知框架，其能力包括：1）将预测扩展至无历史记录的缺失位置，显著提升预测位置的空间覆盖范围，同时减少传感器部署量；2）生成带有不确定性量化的概率预测，以支持下游任务中的风险管理和决策制定。通过在真实数据集上的大量实验，结果表明该方法在预测任务中取得了令人满意的结果，且不确定性量化提供的指标与有无历史数据的位置高度相关。我们进一步证明，该模型可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精度。