Aircraft turnaround time dynamic prediction based on Time Transition Petri Net

Area of Science:

• Operations Research
• Aerospace Engineering

Background:

• Efficient airport operations rely on accurate aircraft turnaround time prediction.
• Coordinating ground services is essential for improving overall airport efficiency.

Purpose of the Study:

• To develop a dynamic prediction method for aircraft turnaround time.
• To improve flight punctuality and airport clearance levels through accurate predictions.

Main Methods:

• Analysis of the aircraft turnaround operation process.
• Development of a Time Transition Petri Net (TTPN) model to describe operations.
• Design of a dynamic turnaround time prediction method using Bayesian theorem and historical flight data.
• Application of Shapiro-Wilke test for prior probability distribution validation.

Main Results:

• The proposed TTPN model effectively describes the flight turnaround process and operational logic.
• The Bayesian prediction method, utilizing actual landing times, demonstrated strong performance.
• Validation against real-world data from a major Chinese airport yielded a root-mean-square error of 3.75 minutes and a mean absolute error of 3.40 minutes.

Conclusions:

• The developed method provides accurate aircraft turnaround time predictions.
• This approach contributes significantly to enhancing flight punctuality and airport operational efficiency.
• The integration of TTPN and Bayesian methods offers a robust solution for airport management.