Multi-Step Ahead Forecasting of Road Condition Using Least Squares Support Vector Regression
نویسندگان
چکیده
Network-level multi-step road condition forecasting is an important step in accurate road maintenance planning, where correct maintenance activities are defined in place and time of road networks. Forecasting methods have developed from engineering models to non-linear machine learning methods that make use of the collected condition and traffic data of the road network. Least Squares Support Vector Regression gives significantly the best results compared to Radial Basis Function networks or multiple linear regression.
منابع مشابه
Forecasting the Portuguese Electricity Consumption Using Least-Squares Support Vector Machines
The subject of this paper is the multi-step prediction of the Portuguese electricity consumption profile up to a 48-hour prediction horizon. In previous work on this subject, the authors have identified a radial basis function neural network one-step-ahead predictive model, which provides very good prediction accuracy and is currently in use at the Portuguese power-grid company. As the model is...
متن کاملDetermination of 137Ba Isotope Abundances in Water Samples by Inductively Coupled Plasma-optical Emission Spectrometry Combined with Least-squares Support Vector Machine Regression
A simple and rapid method for the determination of 137Ba isotope abundances in water samples by inductively coupled plasma-optical emission spectrometry (ICP-OES) coupled with least-squares support vector machine regression (LS-SVM) is reported. By evaluation of emission lines of barium, it was found that the emission line at 493.408 nm provides the best results for the determination...
متن کاملLoad Forecasting Using Fixed-Size Least Squares Support Vector Machines
Based on the Nyström approximation and the primal-dual formulation of Least Squares Support Vector Machines (LS-SVM), it becomes possible to apply a nonlinear model to a large scale regression problem. This is done by using a sparse approximation of the nonlinear mapping induced by the kernel matrix, with an active selection of support vectors based on quadratic Renyi entropy criteria. The meth...
متن کاملShort Term Load Forecasting Using Empirical Mode Decomposition, Wavelet Transform and Support Vector Regression
The Short-term forecasting of electric load plays an important role in designing and operation of power systems. Due to the nature of the short-term electric load time series (nonlinear, non-constant, and non-seasonal), accurate prediction of the load is very challenging. In this article, a method for short-term daily and hourly load forecasting is proposed. In this method, in the first step, t...
متن کاملRevenue forecasting using a least-squares support vector regression model in a fuzzy environment
Revenue forecasting is difficult but essential for companies that want to create high-quality revenue budgets, especially in an uncertain economic environment with changing government policies. Under these conditions, the subjective judgment of decision makers is a crucial factor in making accurate forecasts. This investigation develops a fuzzy least-squares support vector regression model with...
متن کامل