A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

2007 ◽  
pp. 1233-1244 ◽  
Author(s):  
Darong Huang ◽  
Xing-rong Bai
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Traffic Flow ◽  
Control Model ◽  
Wavelet Neural Network ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Traffic Flow Prediction ◽  
Flow Prediction

scholarly journals A Research of Traffic Prediction using Deep Learning Techniques

2019 ◽  
Vol 8 (9S2) ◽  
pp. 725-728
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Transport Systems ◽  
Traffic Data ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Learning Techniques ◽  
Modern Technologies

Traffic data is very important in designing a smart city. Now –a day’smany intelligent transport systems use modern technologies to predict traffic flow, to minimize accidents on road, to predict speed of a vehicle and etc. The traffic flow prediction is an appealing study field. Many techniques of data mining are employed to forecast traffic. Deep learning techniques can be used with technological progress to prevent information from real time. Deep algorithms are discussed to forecast real-world traffic data. When traffic data becomes big data, some techniques to improve the accuracy of trafficprediction are also discussed.

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.

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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