Short - Term Traffic Flow Prediction Based On RBM Neural Network With SVM And K - Means Clustering

Traffic travel in an increasingly busy work life all the more important, how to solve road congestion, reduce accident rates, the world has become a complex problem to be solved. Short-term traffic flow forecasting as a response to the above problem solution because it has the characteristics of real-time and accurate, has been used as a major component of the intelligent transportation system.This paper introduces the basic theory of K- means clustering algorithm, which is characterized by simple and fast, while widely used. Time-intensive sample handling, and sample each other linearly separable classes, then use the clustering effect K- means clustering algorithm obtained will be very good. In support of RBF neural network, the forecasting of traffic flow, has been ideal data.Support vector machine as the machine learning methods in the crowd, it can solve many excellent algorithms practical problems, such as K- means clustering algorithm is very easy to fall into local minima. By introducing support vector machines, we are able to predict traffic flow on, to get more accurate results. Experimental results demonstrate that the use of SVM K- means clustering algorithm, RBF neural network can effectively predict the traffic flow, show that the model is valid.