Research on Flight Ground Service Time Prediction Based on Deep Neural Network

Authors

Zhiwei Xing, 1. School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China;;
Li Biao, 1. School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China;;
Zhu Hui, 1. School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China;;
Luo Qian, 2. Engineering Technology Research Center, the Second Research Institute of CAAC, Chengdu 610041, China;

Abstract

Abstract: Flight ground service time prediction is one of the key issues in improving the airport operational efficiency and decision making capacity. Taking into account the complexity, particularity and uncertainty of the service process, a Gaussian probability model of flight ground service resource in place time is established, a flight ground service time prediction model based on the deep neural network is proposed. According to the regular changes of operational data, the model parameters are adjusted to reducet the generalization error caused by other factors. The research results show that the average absolute error of time prediction under single flight is 4.479 min less than that of the multiple flights, the model evaluation score reaches 94.608, and the prediction accuracy is 3%~5% higher than that of the traditional BP neural network and Bayesian network method.

Recommended Citation

Xing, Zhiwei; Biao, Li; Hui, Zhu; and Qian, Luo (2020) "Research on Flight Ground Service Time Prediction Based on Deep Neural Network," Journal of System Simulation: Vol. 32: Iss. 4, Article 15.
DOI: 10.16182/j.issn1004731x.joss.18-0459
Available at: https://dc-china-simulation.researchcommons.org/journal/vol32/iss4/15

First Page

678

Revised Date

2018-12-10

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.18-0459

Last Page

686

CLC

V351.11;TP183

Recommended Citation

Xing Zhiwei, Li Biao, Zhu Hui, Luo Qian. Research on Flight Ground Service Time Prediction Based on Deep Neural Network[J]. Journal of System Simulation, 2020, 32(4): 678-686.

Corresponding Author

Zhiwei Xing,

DOI

10.16182/j.issn1004731x.joss.18-0459