Irregular flight recovery technique based on accurate transit time prediction

HE Jian; GUO Hongyan; YAO Yuan; BIAN Lei; TANG Hongwu; WANG Diansheng

doi:10.13700/j.bh.1001-5965.2020.0559

Volume 48 Issue 3

Mar. 2022

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Journal of Beijing University of Aeronautics and Astronautics > 2022 > 48(3): 384-393.

Du Jun, Wang Huiyu, Cai Guobiaoet al. Numeric Simulation of Plume Contamination in Vacuum[J]. Journal of Beijing University of Aeronautics and Astronautics, 1999, 25(6): 707-710. (in Chinese)

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HE Jian, GUO Hongyan, YAO Yuan, et al. Irregular flight recovery technique based on accurate transit time prediction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(3): 384-393. doi: 10.13700/j.bh.1001-5965.2020.0559(in Chinese)

Du Jun, Wang Huiyu, Cai Guobiaoet al. Numeric Simulation of Plume Contamination in Vacuum[J]. Journal of Beijing University of Aeronautics and Astronautics, 1999, 25(6): 707-710. (in Chinese)

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Irregular flight recovery technique based on accurate transit time prediction

doi: 10.13700/j.bh.1001-5965.2020.0559

1.
Department of Informatics, Beijing University of Technology, Beijing 100124, China
2.
Travelsky Mobile Technology Limited, Beijing 100029, China

Funds:

National Key R & D Program of China 2020YFB2104400

National Natural Science Foundation of China 61602016

National Natural Science Foundation of China U2033205

More Information

Corresponding author: BIAN Lei, E-mail: bianlei@pku.edu.cn
Received Date: 28 Sep 2020
Accepted Date: 18 Dec 2020
Publish Date: 20 Mar 2022

Abstract

Abstract

In previous studies, the general method for flight recovery problem used fixed flight transit time, rather than considered the result of flight transit time changes in real airports. We propose a LightGBM model to predict accurate transit time based on the airport-flight features from total 235 airports and all flights in China. The numerical results show that our model has 6.783 minutes root mean square error using real flights data. We construct an irregular flight recovery model based on effective transit time, and specifically design a column vector generation algorithm to solve this model. This algorithm can solve the problem of airport traffic flow decrease, airport closure, aircraft maintenance and other irregular conditions under the goal of minimizing flight delays, the number of cancellations, and the number of aircraft changes by canceling, changing the planned time, and replacing aircraft. Tests on actual operating data of airlines prove that the irregular flight recovery method based on transit time prediction is effective. The real case of large-scale flight delays test shows the total delay time can be reduced by 34.2%. The comparison between the spatio-temporal network algorithm and the column vector generation algorithm shows that the proposed flight recovery method also can reduce the recovery cost under the premise of the same recovery result.
- irregular flights,
- flight recovery,
- transit time prediction,
- column generation algorithm,
- recovery cost

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References(24)

References

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