会议专题

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

2010 3rd IEEE International Conference on Computer Science and Information Technology(第三届IEEE计算机科学与信息技术国际会议 ICCSIT 2010)

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)