RETRACTED: A new dynamic pushback control method for reducing fuel-burn costs: Using predicted taxi-out time

Lian, Guan; Zhang, Yaping; Xing, Zhiwei; Luo, Qian; Cheng, Shuiyuan

doi:10.1016/j.cja.2018.12.013

Cited by 7 publications

(10 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The combined effect of taxiing control restrictions and conflicts, and the time spent waiting in this queue at the end of the runway result in additional time (T additional ). Therefore, as shown in Figure 5, the total taxi time of an aircraft can be broken down into reference time and additional taxi time [30], as illustrated in Equation (1) below:…”

Section: Actual Taxi Timementioning

confidence: 99%

“…Once the aircraft joins the queue of departing flights, it awaits its turn for takeoff. Finally, the aircraft follows the tower controller's instructions to approach the runway and complete the takeoff task [30].…”

Section: Type Of Movementmentioning

confidence: 99%

“…Unlike physical measures, planning measures offer a more practical approach to increase the relative capacity and available resources of airports. By carefully planning critical aspects of the aircraft arrival and departure process, these measures can effectively enhance airport operations [1]. The basis of the deployment decision lies in the prediction of the taxi time, and accurate prediction enables effective and efficient deployment.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, precise taxi time prediction plays a pivotal role in aircraft pushback control [3][4][5][6], taxi route optimization [7][8][9][10], runway scheduling, stand assignment optimization [5,11], and other essential aspects of aircraft arrival and departure processes. As we pursue the advancement of intelligent civil aviation and smart airports, the scientific and accurate prediction of aircraft surface taxi time gains tremendous importance in reducing uncertainty in airport surface operations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology

Yin,

Zhang,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Airport arrival and departure movements are characterized by high dynamism, stochasticity, and uncertainty. Therefore, it is of paramount importance to predict and analyze surface taxi time accurately and scientifically. This paper conducts a comprehensive review of existing studies on surface taxi time prediction and analysis. Firstly, the overall research framework of surface taxi time prediction and analysis is categorized from three perspectives: taxi time type, movement type, and modeling method. Then, focusing on the two means of taxi time analytical modeling and simulation modeling, the existing mainstream models and methods are categorized, and the main ideas and scope of application of the various methods are analyzed. Finally, the paper presents the future development direction of surface taxi time prediction prospects. The research results are aimed at providing basic support and methodological guidance for reducing the uncertainty in airport surface operation and enhancing the level of control and decision-making ability of airport surface operation.

show abstract

Section: Actual Taxi Timementioning

confidence: 99%

Section: Type Of Movementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology

Yin,

Zhang,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Nan et al proposed a spatial temporal environment deep learning model that overcomes the drawbacks of the existing machine learning methods [11,12]. Guan et al used least squares regression and queuing theory algorithms to calculate the taxi-out time [13,14]. Shumsky took the traffic flow of aircraft and the number of departing flights as the main influencing factors, respectively used static and dynamic linear models to predict taxi-out time, and verified the conclusion that the prediction effect of dynamic linear model was better [1].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Departure Flights’ Taxi-Out Time Based on Intelligent Algorithm Optimized BP

Xia

Huang

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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.

show abstract

Effect of fuel type on the performance of an aircraft fuel tank oxygen-consuming inerting system

Peng

Feng

et al. 2021

Chinese Journal of Aeronautics

View full text Add to dashboard Cite

RETRACTED: A new dynamic pushback control method for reducing fuel-burn costs: Using predicted taxi-out time

Cited by 7 publications

References 28 publications

Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology

Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology

Prediction of Departure Flights’ Taxi-Out Time Based on Intelligent Algorithm Optimized BP

Effect of fuel type on the performance of an aircraft fuel tank oxygen-consuming inerting system

Contact Info

Product

Resources

About