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    融合不确定性建模的时空交通数据插补方法

    Spatial-Temporal Traffic Data Imputation Method with Uncertainty Modeling

    • 摘要: 交通数据缺失是智能交通系统无法避免的问题之一,对缺失值进行补全和不确定性量化能提高智能交通系统中交通数据挖掘相关任务的精度和可靠性. 然而,目前大多数交通数据插补模型都只能针对缺失值给出点估计,无法量化不确定性,难以满足交通领域对数据可靠性的要求. 而且,现有方法将重点放在了建模交通数据的时空相关性上,却未能在捕获时空相关性的过程中考虑缺失值的影响. 此外,交通数据的不确定性同时受到时间、空间位置、以及数据自身状态的影响,但是现有方法无法全面考虑这些影响的因素. 为了解决这些问题,提出了一种时空不确定性指导的交通数据插补模型(spatial-temporal uncertainty guided traffic data imputation network,STUIN),以自监督训练的方式实现了时空交通数据的插补和对插补结果的不确定性量化. 具体来说,创新地将神经网络的隐状态建模成服从高斯分布的随机变量,借助方差建模隐状态的不确定性,利用基于方差的注意力机制描述不确定性对时空相关性建模的影响;此外,设计了一个新颖的时空不确定性初始化模块,在初始化均值和方差时同时考虑了时间、空间和数据缺失状况多种因素的影响. 在2个交通流量数据集上的实验结果表明STUIN在数据插补和不确定性量化上都达到了最先进的性能.

       

      Abstract: Traffic data missing is one of the unavoidable problems in Intelligent Transportation Systems. Completing missing values and quantifying the uncertainty of them can improve the performance and reliability of traffic data mining tasks in Intelligent Transportation Systems. However, most existing traffic data imputation models mainly focus on point estimation without quantifying the uncertainty, so they cannot meet the need for traffic data reliability in the transportation field. Besides, these methods only focus on modeling spatial-temporal correlation of traffic data, failing to consider the impact of missing values on spatial-temporal correlation. In addition, the uncertainty of traffic data is affected by time, spatial location, and the state of the data, but existing methods cannot comprehensively consider these factors. To address these challenges, we propose a spatial-temporal uncertainty guided traffic data imputation network (STUIN), which simultaneously realizes the imputation of spatial-temporal traffic data and the uncertainty quantification of the imputation results by self-supervised training. Specifically, we innovatively model the hidden states of the neural network as random variables subject to gaussian distributions, use the variances of gaussian distributions to model the uncertainty of the hidden states, and introduce a variance-based attention mechanism to characterize the effect of uncertainty on modeling spatiotemporal correlations. In addition, we design a novel spatial-temporal uncertainty initialization module, which incorporates the influence of time, space and missing values when initializing the means and variances of the gaussian distributions. Experiments on two traffic flow datasets show that STUIN achieves state-of-the-art performance on both the data imputation and uncertainty quantification tasks.

       

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