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.