首页|Civil Aviation Flight University of China Details Findings in Machine Learning ( Airspace Situation Analysis of Terminal Area Traffic Flow Prediction Based On Bi g Data and Machine Learning Methods)
Civil Aviation Flight University of China Details Findings in Machine Learning ( Airspace Situation Analysis of Terminal Area Traffic Flow Prediction Based On Bi g Data and Machine Learning Methods)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating in Guanghan, People's Re public of China, by NewsRx journalists, research stated, "Realtime and accurate prediction of terminal area arrival traffic flow is a key issue for terminal ar ea traffic management. In this paper, we study the advantages and disadvantages of traditional dynamics -based prediction methods and time -series based predict ion methods in the first step." Financial supporters for this research include Civil Aviation Flight University of China Fund Project, Civil Aviation Education Fund Project, Sichuan Province F und Project. The news reporters obtained a quote from the research from the Civil Aviation Fl ight University of China, "Taking the advantages of the two type of methods, a t erminal area arrival flow prediction framework based on airspace situation is pr oposed. In our method, the airspace situation is used as the machine learning fe ature to estimate the number of arrival aircraft. In addition, also based on mac hine learning approach, a correction stage is added to the algorithm to improve the accuracy of the prediction. ADS -B data collected from the terminal area of Chengdu is used to study the prediction accuracy based on different machine lear ning algorithms in the proposed framework. Experimental results show that the pr oposed method can predict the air traffic flow accurately. The average absolute error is only 0.35 aircraft/15 min, the root mean square error is 0.67 aircraft/ 15 min, and the maximum absolute error is 2 aircraft/15 min."
GuanghanPeople's Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningCivil Aviation Flight Univ ersity of China
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