Research on coordinated slot parameter profiles based on flight delay


	prediction under multiple weather patterns

Abstract

Abstract:

Constructing a reasonable 18—24 h coordinated slot parameter profile for busy airports that aligns with normality

expectations, used for publishing capacity and facilitating seasonal slot adjustments, remains an unresolved issue

in aviation slot management. This paper develops a regression prediction model for slot structure using historical

flight and weather data, employing K-means clustering and partial least squares regression, while applying

ensemble learning to forecast flight delay levels. The results show that random forests exhibit good performance in

both regression and prediction, and can combine flight delay predictions to obtain the upper and lower limits of

the coordinated slot parameter profile as the slot coordination parameter interval. The results are verified through

simulation, ensuring that the flight schedule within this interval maintains an average delay level of less than 15

min, leading to the proposal of recommended slot coordination parameters. This paper provides refined decision support information for relevant departments in slot management and air traffic flow management during different

strategic and tactical periods, as well as for airports and airlines in evaluating delay risks, adjusting slot arrangements, allocating capacity, and planning support resources.

Key words:

air traffic, multiple weather patterns, flight slots, flight delay, ensemble learning

CLC Number:

V352

GAO Wei , PANG Dingying, .

Research on coordinated slot parameter profiles based on flight delay

prediction under multiple weather patterns

[J]. Journal of Civil Aviation University of China, 2025, 43(6): 24-30.

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Research on coordinated slot parameter profiles based on flight delay

prediction under multiple weather patterns

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