Improved Intelligent Method for Traffic Flow Prediction Based on Artificial Neural Networks and Ant Colony Optimization

Real time traffic flow is often difficult to predict precisely because of the complexity, nonlinearity and uncertainty characteristics of the traffic flow data. Intelligent prediction methods such as artificial neural network (ANN), support vector machine (SVM), etc. have been proven effective to discover the nonlinear information hidden in the traffic flow data. Nevertheless, 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 artificial neural networks (ANN) with ant colony optimization (ACO), called ANN-ACO, by exploiting complementary advantages of both approaches. Firstly, this method uses the ANN theory for data reduction pretreatment, and then constructs the traffic flow prediction model based on ACO according to the information structure. The analysis results show that the proposed method can extract the underlying rules of the testing data and decrease prediction error or better when compared with single ANN or ACO approach. 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.