Short-term traffic flow prediction is important to realize real-time traffic instruction. However, due to the existing strong nonlinearity and non-stationarity in short-term traffic volume data, it is hard to obtain a satisfactory result through the traditional method. To this end, this paper develops an innovative hybrid method based on the time varying filtering based empirical mode decomposition (TVF-EMD) and least square support vector machine (LSSVM). Specifically, TVF-EMD is firstly used to deal with the implied non-stationarity in the original data by decomposing them into several different subseries. Then, the LSSVM models are established for each subseries to capture the linear and nonlinear characteristics embedded in the original data, and the corresponding prediction results are superimposed to obtain the final one. Finally, case studies based on two groups of data measured from an arterial road intersection are employed to evaluate the performance of the proposed method. The experimental results indicate it outperforms the other involved models. For example, compared with the LSSVM model, the average improvements by the proposed method in terms of the indexes of mean absolute error, mean relative percentage error, root mean square error and root mean square relative error are 7.397, 15.832%, 10.707 and 24.471%, respectively.

Teachers and students are the two basic elements in educational activities. Students are educated but are not exactly passive recipients of education. With subjective initiative, all educational impacts must be through the initiative of students to achieve the desired effect. Therefore, all activities of education must start from mobilizing students’ initiative and motivation so that they have sufficient motivation to learn actively and well. The effective analysis of employment data, at the statistical level of data analysis, is a favorable basis to support the influence of teachers on students. However, most of the previous methods are C4.5 algorithms, decision tree generation algorithms based on rough sets, etc., which are commonly used for employment data analysis. None of them can sufficiently deal with the problem of different decision accuracy requirements and noise adaptability. In this paper, we analyze the employment data of a university in 2012 as an example and compare the analysis results with those of the C4.5 algorithm and decision tree generation algorithm based on a rough set. The results show that the decision tree algorithm based on the multiscale rough set model generates a simple decision tree structure. In addition, our methods do not have indistinguishable datasets and are fast in terms of computing. This study provides an effective guide to the relevance of teachers’ cognitive abilities and teaching motivations for students’ employment.

Strong nonstationary and nonlinearity are the main characteristics in the short-term traffic flow data, which frustrates traditional methods (e.g., autoregressive integrated moving average and deep belief network) to provide a satisfactory prediction. To address the above problem, a novel forecasting method, which is composed of a secondary decomposition technique and extreme learning machine, is proposed in this study. This developed technique is a hybrid of time-varying filtering-empirical mode decomposition (TVF-EMD) and local mean decomposition (LMD), which not only can effectively handle the above complex data features by decomposing them into several regular subsets but also produce the smoother subseries that is beneficial to prediction. To verify the effectiveness of the proposed method, a case study based on two groups of actual traffic flow data with different characteristics is performed. Meanwhile, several single models and hybrid models based on the other decomposition methods (e.g., EMD and variational mode decomposition) are considered benchmark models. The experimental results reveal that the proposed model presents the best performance. For example, compared with the TVF-EMD-based method, the improvement by the proposed approach reaches 33.3% in terms of the evaluation criterion of mean absolute percentage error.