Missing data is inevitable and ubiquitous in intelligent transportation systems (ITSs). A handful of completion methods have been proposed, among which the tensor-based models have been shown to be the most advantageous for missing traffic data imputation. Despite their superior imputation accuracies, the adoption of these imputed data is not uniform in modern ITSs applications. The primary goal of this paper is to explore how to use tensor completion methods to support ITSs. In particular, we study how to improve traffic flow prediction accuracy under different missing scenarios. Specifically, three common missing scenarios including element-wise random missing, time-structured missing, and space-structured missing are considered. Four classical tensor completion models including Smooth PARAFAC Decomposition based Completion (SPC), CP Decomposition-based (CP-WOPT) Completion, Tucker Decomposition-based Completion (TDI), and High-accuracy Low-rank Tensor Completion (HaLRTC) are used to impute the missing data. Four well-known prediction methods including Support Vector Regression (SVR), K-nearest Neighbor (KNN), Gradient Boost Regression Tree (GBRT), and Long Short-term Memory (LSTM) are tested. The simple mean value interpolation completed traffic data is regarded as the baseline data. The extensive experiments show that improvements of traffic flow prediction can be achieved by increasing missing traffic data imputation accuracy at most cases. Interestingly we find that prediction accuracy cannot be improved by an imputation model when the sparsely observed training datasets already provide sufficient training samples.

Accurate spatial-temporal traffic flow forecasting is essential for helping traffic managers take control measures and drivers to choose the optimal travel routes. Recently, graph convolutional networks (GCNs) have been widely used in traffic flow prediction owing to their powerful ability to capture spatial-temporal dependencies. However, designing the spatial-temporal graph adjacency matrix, which is essential to the success of GCNs remains an open question. This paper proposes a GCN-based traffic flow forecasting method that reconstructs the binary adjacency matrix via tensor decomposition. We first reformulate the spatial-temporal fusion graph adjacency matrix into a three-way adjacency tensor. Then, we use Tucker decomposition to reconstruct the adjacency tensor, encoding more informative and global spatial-temporal dependencies. Finally, we propose multiple Spatial-temporal Tensor Graph Convolution layers that assemble a Spatial-temporal Synchronous Graph Convolutional module for localized spatial-temporal correlations learning and a Dilated Convolution module for global correlations learning in parallel. This enables the comprehensive spatial-temporal dependencies of the road network to be aggregated and learned. Experimental results on four open-access datasets demonstrate that the proposed model outperforms state-of-the-art approaches in terms of prediction performances.

The incidence of prostate cancer increases annually. Prostate cancer is an underreported and emerging problem in China. We conducted a cross-sectional study of 392 eligible patients from 710 men with prostate cancer or benign prostatic hyperplasia between 2000 and 2003. For total prostate-specific antigen, age, three diameters of prostate, prostate volume and prostate-specific antigen density seven indices, analysis of variance and t test were used to analyze the difference between the groups. A decision tree with pruning was established using the prostate-specific antigen density, age and transversal diameter of the prostate to screen the patient with prostate cancer. According to the established decision tree model, prostate-specific antigen density was the most important factor affecting the occurrence of prostate cancer. In elderly people over the age of 83 years, the transverse diameter of prostate cancer was smaller than that of benign prostatic hyperplasia, with prostate-specific antigen density less than $0.49\text{ng/L}^2$ . No additional index was introduced, and the detection rate of prostate cancer was 86.6 %.The specificity was enhanced to 78.1%.