Deep learning based short-term air traffic flow prediction considering temporal–spatial correlation

2019 ◽  
Vol 93 ◽  
pp. 105113 ◽  
Author(s):  
Yi Lin ◽  
Jian-wei Zhang ◽  
Hong Liu
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Spatial Correlation ◽  
Air Traffic ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Air Traffic Flow

scholarly journals Research on the Air Traffic Flow Prediction Using a Deep Learning Approach

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 148019-148030
Author(s):  
Hong Liu ◽  
Yi Lin ◽  
Zhengmao Chen ◽  
Dongyue Guo ◽  
Jianwei Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Air Traffic ◽  
Learning Approach ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Air Traffic Flow

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.

Research of Air Traffic Flow Forecasts Based on BP Neural Network

2013 ◽  
Vol 671-674 ◽  
pp. 2912-2915
Author(s):  
Ming Qiang Chen ◽  
Jun Hong Feng
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Back Propagation ◽  
Good Method ◽  
Back Propagation Neural Network ◽  
Air Traffic ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
The Neural Network ◽  
Air Traffic Flow

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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