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.