Spatial-temporal uncertainty-aware graph networks for promoting accuracy and reliability of traffic forecasting

计算机科学 可靠性(半导体) 不确定度量化 数据挖掘 过程(计算) 图形 机器学习 人工智能 量子力学 理论计算机科学 操作系统 物理 功率(物理)
作者
Xiyuan Jin,Jing Wang,Shengnan Guo,Tonglong Wei,Yiji Zhao,Yuxia Lin,Huaiyu Wan
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:238: 122143-122143
标识
DOI:10.1016/j.eswa.2023.122143
摘要

Providing both point estimation and uncertainty quantification for traffic forecasting is crucial for supporting accurate and reliable services in intelligent transportation systems. However, the majority of existing traffic forecasting works mainly focus on point estimation without quantifying the uncertainty of predictions. Meanwhile, existing uncertainty quantification (UQ) methods fail to capture the inherent static characteristics of traffic uncertainty along both the spatial and temporal dimensions. Directly equipping the traffic forecasting works with uncertainty quantification techniques may even damage the prediction accuracy. In this paper, we propose a novel traffic forecasting model aiming at providing point estimation and uncertainty quantification simultaneously, called STUP. Compared to the traditional graph convolution networks (GCNs), our framework is able to incorporate uncertainty quantification into traffic forecasting to further improve forecasting performance. Specifically, we first develop an adaptive strategy to initialize uncertainty distribution. Then a kind of spatial–temporal uncertainty layer is carefully designed to model the evolution process of both the traffic state and its corresponding uncertainty, along with a gated adjusting unit to avoid error information propagation. Finally, we propose a novel constraint loss to further help improve the forecasting accuracy and to alleviate the training difficulty caused by the lack of uncertainty labels. Experiments on five real-world traffic datasets demonstrate that STUP outperforms the state-of-the-art baselines on both the traffic prediction task and uncertainty quantification task.
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