Research of Air Traffic Flow Forecasts Based on BP Neural Network

Ming Qiang Chen; Jun Hong Feng

doi:10.4028/www.scientific.net/AMR.671-674.2912

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 671-674Research of Air Traffic Flow Forecasts Based on BP...

Research of Air Traffic Flow Forecasts Based on BP Neural Network

Abstract:

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 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 back propagation neural network.

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Periodical:

Advanced Materials Research (Volumes 671-674)

Pages:

2912-2915

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.671-674.2912

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Online since:

March 2013

Authors:

Ming Qiang Chen, Jun Hong Feng

Keywords:

Airport Traffic Flow, BP Neural Network (BPNN), Prediction Model

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