Title :
Notice of Retraction
Prediction of traffic flow at intersection based on self-adaptive neural network
Author :
Haixiang Dong ; Tang Jingjing
Author_Institution :
Sch. of Inf. Eng., North China Univ. of Water Conservancy & Electr., Power, Zhengzhou, China
Abstract :
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
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.
Keywords :
computational complexity; convergence; gradient methods; neural nets; traffic engineering computing; computational complexity; convergence speed; goodness-of-fit; gradient descent algorithms; selfadaptive neural network; traffic flow prediction; urban traffic management; Adaptation model; Adaptive systems; Self-adaptive neural network; genetic algorithm; traffic volume predictiont; wavelet neural network;
Conference_Titel :
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-5537-9
DOI :
10.1109/ICCSIT.2010.5564119
