A New Feature Extraction And Its Application In Road Performance Analysis

There are wealth of census data in highway management information system. The census data can be used for comprehensive evaluation of pavement performance, and used to forecast pavement performance as well. In essence, this is a regression analysis of census data, and it has following characteristics.1.The regression should be nonlinear in order to ensure the accuracy of evaluation and prediction.2.The regression can be applied to small sample data sets.3.The regression Also can avoid the effect of the noise in data.4.The regression model should be an explicit function which is simple and easy to analyze the causality. The evaluation model and the forecasting evaluation model can provide a strong base for decision-making on road maintenance.In such practical problems as above, the existing regression methods are ineffective. Such as support vector regression trained by small sample data set is easy to fall in overfitting. The precision of regression function is low. The degree of regression function is distorted. Using neural network method cannot get an explicit function, and can not reflect the relationship between input and output. To solve these problems, two new features extraction methods are proposed. Using the new methods in highway management information system, we get a new comprehensive evaluation and prediction of pavement performance.The innovations of this paper are as following:(1) A feature extraction method based on matrix similarity measurement, genetic algorithm and linear support vector regression is proposed in this paper. Firstly, the nonlinear space is selected by using matrix similarity measurement. Then features are extracted from the nonlinear space by GA. A regression function is gotten by linear SVR. Experiments prove that the precision is higher than other methods when the sample size is small. The regression function gotten by this method has a simple and clear form. This facilitates the causality analysis. It is intuitive to set input-output model. In addition, it is proved that the matrix similarity measurement is effective to control VC dimension.(2) A sequence minimization based on mixed kernel, matrix similarity measurement and kernel principal component analysis is proposed. The mixed kernel is used in KPCA. The parameters of the mixed kernel are determined by GA, while the matrix similarity measurement serves as the fitness. So one can control kernel complexity as much as possible. A sequence minimization method is used to choose principal component, and the dimension of input space is reduced further. It will not increase the VC dimension of the learning machine because sequence minimization method is a linear SVM. Experiments prove that this method is better than previous methods.(3) The feature extraction method based on matrix similarity measurement, GA and linear SVR is applied to pavement performance evaluation. The difficulties caused by small training data set is avoided. A simple polynomial function can be gotten to express the relationship between pavement performance and all kinds of damage on road. This function makes it easy to analyze the causality.(4) The feature extraction method based on matrix similarity measurement, GA and linear SVR was applied to pavement performance prediction. A simple polynomial function is clear to express the relationship between pavement performance and all kinds of factor. This function provides a sound basis for decision-making on road maintenance.