Short-term traffic flow prediction based on rough set and support vector machine

According to the highly complexity, nonlinearity and uncertainty of traffic flow, a single prediction model is difficult to ensure the prediction accuracy and efficiency. To overcome the lack of the single prediction method, this paper uses a prediction method that combining rough set with support vector machine, called RS-SVM, by exploiting complementary advantages of both approaches. Firstly, this method uses the rough set theory for data reduction pretreatment, and then constructs the traffic flow prediction model based on support vector machine according to the information structure. The results of the model are better than the BP Neural network and single support vector machine model. Besides, the combined prediction model not only has fault tolerant and anti-jamming capability, but also can shorten the operation time and improve the speed of the system and also forecast accuracy. Hence, it can be used to forecast real-time traffic flow.