Flight time-driven characteristics of departing passenger aggregation

Qian Qin; Xinwei Ding; Yechen Wang; Qing Wang

doi:10.1117/12.2692455

23 August 2023 Flight time-driven characteristics of departing passenger aggregation

Qian Qin, Xinwei Ding, Yechen Wang, Qing Wang

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 127842T (2023) https://doi.org/10.1117/12.2692455
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

In the period of post-epidemic, the civil aviation industry is in the stage of steady recovery and rapid growth, and the degradation of airport operation efficiency and service quality caused by the high saturation of airport terminal integrated transport hub operation has become a critical issue in the construction of Type IV airport. Since the existing models cannot accurately perceive the passenger gathering characteristics, resulting in low utilization of service resource allocation and frequent passenger congestion, studying the gathering characteristics of departing passengers becomes one of the important means to improve passenger satisfaction. Based on the all-day flight scheduling plan, we extend the study of passenger aggregation pattern during peak hours to the whole day range, and establish a model of passenger aggregation characteristics based on mixed Gaussian distribution for all hours of departure. Under the second-order mixed Gaussian model, the parameters in the model are estimated using the EM algorithm, and validated and analyzed by multiple sets of simulation experiments including weekdays and non-weekdays. The obtained results show that the model has high accuracy for the number of passengers arriving during all hours, especially for the peak hours, which provides an important role in improving the service quality of the airport terminal integrated transport hub, accelerating the operational efficiency of the airport, and transforming to a digital airport. The results show that the model has a high accuracy for all-time passenger arrivals, especially for peak periods, and provides an important role in improving airport service quality, accelerating airport terminal integrated transport hub operation efficiency, and transforming to a digital airport.

Citation Download Citation

Qian Qin, Xinwei Ding, Yechen Wang, and Qing Wang "Flight time-driven characteristics of departing passenger aggregation", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 127842T (23 August 2023); https://doi.org/10.1117/12.2692455

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KEYWORDS

Expectation maximization algorithms

Data modeling

Mixtures

Statistical modeling

Statistical analysis

Transportation

Computer simulations

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