| Hi-index | 0.01 |
As intelligent transportation systems (ITS) are implemented widely throughout the world, managers of transportation systems have access to large amounts of real-time status data. A variety of methods and techniques have been developed to forecast traffic flow. The traffic flow forecasting model based on neural network has been applied widely in ITS because of its high forecasting accuracy and self-learning ability. But the problems of neural network such as the difficult of designing optimal structure and weak global searching ability limit seriously its applications. The paper proposes traffic flow forecasting based on genetic neural network. The genetic algorithm, which has a powerful global exploration capability, is applied to solve the problem of tuning both network structure and parameters of a feedforward neural network. First, the authors introduce the genetic neural network algorithm in detail. Then, the presented approach is effectively applied to solve traffic flow forecasting. The simulation experiments show that the presented traffic flow forecasting based on genetic neural network can simplify the structure of neural network greatly and improve the forecasting accuracy significantly.