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<p>Passenger congestion in airport check-in areas fluctuates dynamically over time, leading to significant inefficiencies in resource allocation and negative impacts on passenger experience. A critical challenge is accurately modeling the combined effects of non-stationary arrivals and passenger heterogeneity (with and without checked baggage). This study addresses this by modeling the check-in system using multiple parallel <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\( G\left( t \right)/G_{2} \left( t \right)/1 \)</EquationSource> </InlineEquation> non-stationary queuing models. We propose an extended Stationary Backlog-Carryover (SBC) approximation method that specifically incorporates distinct service time distributions for two passenger types under time-varying demand. Numerical case studies validate the approach, demonstrating an average deviation of approximately 14% compared to discrete-event simulation. This level of accuracy, combined with high computational efficiency, validates the model’s suitability for practical, rapid decision-making in airport operations planning.</p>

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Non-stationary performance analysis of airport check-in systems with passenger heterogeneity

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Hui-Yu Zhang,
  • Jia-Ming Tian,
  • Yong Liao,
  • Jia-Jun Wu

摘要

Passenger congestion in airport check-in areas fluctuates dynamically over time, leading to significant inefficiencies in resource allocation and negative impacts on passenger experience. A critical challenge is accurately modeling the combined effects of non-stationary arrivals and passenger heterogeneity (with and without checked baggage). This study addresses this by modeling the check-in system using multiple parallel \( G\left( t \right)/G_{2} \left( t \right)/1 \) non-stationary queuing models. We propose an extended Stationary Backlog-Carryover (SBC) approximation method that specifically incorporates distinct service time distributions for two passenger types under time-varying demand. Numerical case studies validate the approach, demonstrating an average deviation of approximately 14% compared to discrete-event simulation. This level of accuracy, combined with high computational efficiency, validates the model’s suitability for practical, rapid decision-making in airport operations planning.