Abstract
Taxi-out time is the main performance index to evaluate the operational efficiency of major airports. Scientifically and accurately predicting the taxi-out time of departure flights is very important to improve the operational efficiency and coordination decision-making ability of airport. Firstly, the quantifiable influence factors of taxi-out time and their correlation is analyzed, including the number of departure flights, the number of arrival flights, the number of flights pushed back in the same period, the taxi-out time by half-hour in average, the taxi distance, and the number of turns, etc. And then a prediction model of departure flights’ taxi-out time based on BP is constructed. Because the traditional BP neural network is sensitive to the initial weight and threshold and has poor accuracy and stability, the taxi-out time prediction model of BP neural network optimized by intelligent algorithm is proposed. Genetic algorithm (GA) and sparrow search algorithm (SSA) are used to obtain the optimal weight and threshold of BP neural network, which is verified by the actual operation data of a major airport in central and southern China for two weeks. The results show that ① the taxi-out time is strongly correlated with the airport surface traffic flow, moderately correlated with the average taxi-out time, and weakly correlated with the taxi distance and the number of turns. ② The predicted outcome of the 4-element combination prediction model which considers strong correlation and medium correlation factors is the best. After adding weak correlation factors, the prediction accuracy is reduced. ③ By obtaining the local optimal weight and threshold of neural network, intelligent optimization algorithm can effectively improve the accuracy of departure flights’ taxi-out time prediction results. ④ The prediction result of BP neural network optimized based on GA is 1.79% higher than that of MAPE before optimization, MAE is reduced by 7.4 s, and RMSE is reduced by 6.93 s. The prediction result of BP neural network optimized based on SSA is 3.05% higher than that of MAPE before optimization, MAE is reduced by 16.55 s, and RMSE is reduced by 14.31 s. Therefore, Sparrow search algorithm has better optimization effect on the model than genetic algorithm.
Highlights
3 By obtaining the local optimal weight and threshold of neural network, intelligent optimization algorithm can effectively improve the accuracy of departure flights’ taxi-out time prediction results. 4 e prediction result of BP neural network optimized based on Genetic algorithm (GA) is 1.79% higher than that of MAPE before optimization, MAE is reduced by 7.4 s, and root mean square error (RMSE) is reduced by 6.93 s. e prediction result of BP neural network optimized based on sparrow search algorithm (SSA) is 3.05% higher than that of MAPE before optimization, MAE is reduced by 16.55 s, and RMSE is reduced by 14.31 s. erefore, Sparrow search algorithm has better optimization effect on the model than genetic algorithm
The taxi-out time is affected by flow control, bad weather, airlines, controllers, passengers, number of turns, and other factors, but these factors are not quantifiable or small, so they will not be considered in this paper. erefore, the main quantifiable factors of departure flight taxi-out time include the number of departure flights launched in the same period, the number of takeoff flights taxiing in the same period, the number of landing flights taxiing in the same period, taxi distance, and the number of turns
By sorting out the data, delete the duplicate and abnormal data, and obtain the number of departure flights taxiing at the same time, the number of inbound flights taxiing at the same time, the number of departure flights launched at the same time, the half-hour average taxi-out time, taxi distance, the number of turns, and the actual taxi-out time according to formulas (2)∼(6). rough the correlation analysis of the sample data, Figure 1 can be obtained
Summary
2 e prediction method based on fast simulation [2]: is method is based on the mature simulation platforms such as Simmod and Airtop to make models about the surface operation process of departure flights. It has the disadvantages of high cost and long time to adjust the simulation model. Because the traditional BP neural network is sensitive to the initial weight and threshold and has poor accuracy and stability, a taxi-out time prediction model based on intelligent algorithm is proposed. Genetic algorithm (GA) and sparrow search algorithm (SSA) are used to obtain the optimal weight and threshold of BP neural network, which is verified by the actual operation data of a major airport in central and southern China for two weeks. e research objective is to verify which intelligent optimization algorithm is more effective for the optimization of BP neural network
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