Traffic flow forecasting by grey model and least squares support vector machine algorithm

The accuracy of traffic flow forecasting plays an important role in the field of modern Intelligent Transportation Systems (ITS). Summarizing the existing forecasting models and considering the characteristics of the traffic itself such as nonlinearity, complexity and uncertainty, the prediction accuracy for traffic flow by the traditional method is often lower. In this paper, a combined forecasting method (Grey Model and Least Squares Support Vector Machine, GM-LSSVM) based on grey model (GM) and least squares support vector machine (LSSVM) algorithm was proposed. In the proposed forecasting model the advantages of grey model such as less raw data to be required, simple to model and convenient to calculate are fully utilized and the features of LSSVM such as strong generalization ability, good nonlinear fitting ability and less samples to be required are combined, thus the forecasting accuracy can be improved. The combined model was validated on real traffic data and simulation results show that the proposed combination forecasting method is effective and practicable.