Short-Time Traffic Flow Prediction Using Fuzzy Wavelet Neural Network Based on Master-Slave PSO

2008 ◽  
Author(s):  
Wanxia Yu ◽  
Taihang Du ◽  
Weicun Zhang
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Wavelet Neural Network ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Fuzzy Wavelet Neural Network ◽  
Short Time

scholarly journals Short-term traffic flow prediction based on cuckoo search-wavelet neural network

2021 ◽  
Author(s):  
Haibo Lv ◽  
Yuheng Kang ◽  
Zhou Shen
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Network Model ◽  
Cuckoo Search ◽  
Wavelet Neural Network ◽  
Urban Traffic ◽  
Prediction Algorithm ◽  
Step Size ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Abstract The nonlinear fluctuation and uncertainty that characterize urban traffic flow are well-known. An Improved Cuckoo Search-Wavelet Neural Network (ICS-WNN) prediction model for urban traffic flow is suggested in order to increase the accuracy of traffic flow predictions. After the original traffic flow data have been cleaned up and normalized, the traffic flow prediction network model is built by optimizing the wavelet neural network weights and wavelet shrinkage and translation factors based on the adaptive step size and discovery probability of the cuckoo algorithm, and then adding the neural network momentum factor. The traffic flow prediction network model is built in two stages. The results of the experimental simulations demonstrate that the ICS-WNN prediction algorithm has a better fit and accuracy than numerous common optimization prediction techniques, which is encouraging.

The Model of Short-Time Traffic Flow Prediction on High-Grade Highway

2011 ◽  
Vol 255-260 ◽  
pp. 4128-4132
Author(s):  
Hong Chen ◽  
Yu Wei Yuan ◽  
Juan Sun ◽  
Na Bao
Keyword(s):  
Neural Network ◽  
Wavelet Analysis ◽  
Traffic Flow ◽  
Rbf Neural Network ◽  
High Grade ◽  
Traffic Flow Prediction ◽  
Discontinuous Flow ◽  
Flow Prediction ◽  
The Difference ◽  
Short Time

In order to study the short-time traffic flow prediction on high-grade highway, the article proposed a model based on wavelet analysis and RBF neural network. Aiming to the traffic flow’s characteristic of highway, the study focus on three facet: network topology, the difference of continuous flow and discontinuous flow , the flow of lanes’ uplink and downlink are not equal. Thus the article use the wavelet analysis to do data preprocessing, then structure the model of short-term traffic flow prediction based on RBF neural network. The experiment result shows that the new hybrid model adapt to high-grade highway, and model considering traffic flow characteristic is better than the model which is not. Meanwhile the model has the higher precision of prediction.

Short-Term Traffic Flow Prediction Model of Wavelet Neural Network Based on Mind Evolutionary Algorithm

2018 ◽  
Vol 32 (12) ◽  
pp. 1850041 ◽  
Author(s):  
Liqiang Xu ◽  
Xuedong Du ◽  
Binguo Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Evolutionary Algorithm ◽  
Traffic Flow ◽  
Prediction Accuracy ◽  
Wavelet Neural Network ◽  
Short Term ◽  
Connection Weight ◽  
Traffic Flow Prediction ◽  
Flow Prediction

This paper introduces mind evolutionary algorithm (MEA) into the application of short-term traffic flow prediction, and proposes a short-term traffic flow prediction model of wavelet neural network based on mind evolutionary algorithm (MEA-WNN). The optimal connection weight and wavelet parameters of wavelet neural network (WNN) are searched globally by MEA, and the convergence capacity of wavelet neural network is improved. The experimental data show that, compared with the prediction model of the traditional WNN and the WNN based on genetic algorithm (GA-WNN), the prediction model of MEA-WNN has higher global prediction accuracy.

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