Traffic Flow Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm

The accuracy of traffic flow forecasting plays an important role in the field of modern Intelligent Transportation Systems (ITS). The least squares support vector machine (LSSVM) has been shown to provide a strong potential in forecasting problems, particularly by using appropriate heuristic algorithms to determine the value of its two parameters. However, the disadvantage of these meta-heuristics is that it is difficult to understand and slowly achieve the global optimal solution. As a new heuristic algorithm, the fruit fly optimization algorithm (FOA) has the advantages of easy to understand and quickly converge to the global optimal solution. Therefore, in order to improve the prediction performance of the model, this paper presents a traffic flow prediction model based on least squares support vector machine and automatically determines the least squares support vector machine model with two parameters in the appropriate value by FOA. The experiment results show that the LSSVM combined with FOA (LSSVM-FOA) perform better than other methods, namely single LSSVM model, RBF neural network (RBFNN) and LSSVM combined with particle swarm optimization algorithm (LSSVM-PSO).