Novel Model Based on Wavelet Transform and GA-fuzzy Neural Network Applied to Short Time Traffic Flow Prediction

2011 ◽  
Vol 10 (11) ◽  
pp. 2105-2111 ◽  
Author(s):  
Yuesheng Gu ◽  
Yancui Li ◽  
Jiucheng Xu ◽  
Yanpei Liu
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Traffic Flow ◽  
Fuzzy Neural Network ◽  
Traffic Flow Prediction ◽  
Model Based ◽  
Flow Prediction ◽  
Fuzzy Neural ◽  
Short Time ◽  
Novel Model

Using Hybrid Fuzzy Neural Network to Improve the Accuracy of Air Traffic Flow Forecasts

2013 ◽  
Vol 333-335 ◽  
pp. 1422-1425
Author(s):  
Ming Qiang Chen
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Fuzzy Neural Network ◽  
Good Method ◽  
Air Traffic ◽  
Traffic Flow Prediction ◽  
Air Traffic Controllers ◽  
Flow Prediction ◽  
Air Traffic Flow ◽  
Fuzzy Neural

Air traffic is increasing worldwide at a steady annual rate, and airport congestion is already a major issue for air traffic controllers. The traditional method of traffic flow prediction is difficult to adapt to complex air traffic conditions. Due to its self-learning, self-organizing, self-adaptive and anti-jamming capability, the hybrid fuzzy neural network can predict more effectively the air traffic flow than the traditional methods can. A good method for training is an important problem in the prediction of air traffic flow with neural network. This paper will try to find a new model to solve the traffic flow prediction problem by hybrid fuzzy neural network.

Application of a Traffic Flow Prediction Model Based on Neural Network in Intelligent Vehicle Management

2019 ◽  
Vol 33 (03) ◽  
pp. 1959009 ◽  
Author(s):  
Yang Guo ◽  
Lu Lu
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Forecasting Model ◽  
Intelligent Vehicle ◽  
Traffic Flow Prediction ◽  
Model Based ◽  
Flow Prediction ◽  
Combination Forecasting ◽  
Combination Forecasting Model ◽  
Vehicle Management

The ultimate direction of intelligent vehicle management is to achieve artificial intelligence (AI), and data mining is an important supporting technology for AI. The adoption of new AI technology can effectively improve operational efficiency and safety, especially in terms of performance. This paper takes the researches on traffic jam as an example and proposes one algorithm for combination forecasting model based on a segmentation algorithm for traffic flow sequence and BP neural network prediction. In this paper, it also introduces the traffic flow clustering analysis and mining algorithms for congestion events at the intersections. The blocking point algorithm is improved, and experimental analysis is performed through samples. Experimental results show that the algorithm use for combination forecasting model can greatly improve the real-time performance of short-term traffic flow prediction and significantly reduce the prediction error rate. Therefore, this algorithm has practical and innovative significance in the field of intelligent vehicle management.

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.

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