Prediction of Traffic Flow at Intersection Based on Self-Adaptive Neural Network
Traffic flow prediction plays an important role in urban traffic management and control. Traditional prediction methods are mostly difficult to meet the high complexity, randomness and uncertainty characteristics of urban traffic flow. In this paper, a new prediction model is proposed based on self-adaptive neural network. Compared with other methods, it possesses the advantages of low computational complexity, fast convergence speed, high goodness-of-fit and so on. Furthermore, it overcomes the drawbacks of vibration effects and easy falling into local minimum caused by single gradient descent algorithms. Simulation results prove the validity of this prediction model.
Self-adaptive neural network traffic volume predictiont genetic algorithm wavelet neural network
Dong Haixiang Tang Jingjing
School of Information Engineering North China University of Water Conservancy & Electric Power Zheng School of Mechanical & Electrical Engineering Henan University Technology Zhengzhou China
国际会议
成都
英文
95-98
2010-07-07(万方平台首次上网日期,不代表论文的发表时间)