  Airport public transport systems are plagued by passenger queue congestion,
imposing a substandard travel experience and unexpected delays. To address this
issue, this paper proposes a bi-level programming for optimizing queueing
network in airport access based on passenger choice behavior. For this purpose,
we derive queueing network for airport public transport system, which include
the taxi, bus, and subway. Then, we propose a bi-level programming model for
optimizing queueing network. The lower level subprogram is designed to
correspond to the profit maximization principle for passenger transport mode
choice behavior, while the upper level subprogram is designed to minimize the
maximum number of passengers waiting to be served. Decision makers consider
imposing queue tolls on passengers to incentivize them to change their choice
and achieve the goal of avoiding congestion. Finally, we develop the successive
weighted averages (MSWA) method to solve the lower subprogram's passenger share
rates and the ant lion optimization (ALO) method to solve the bi-level
program's queue toll scheme for upper-level objectives. We prove the
effectiveness of the proposed method on two situations of simulation, daytime
and evening cases. The numerical results highlight that our strategy can
alleviate queue congestion for both scenarios and effectively improve
evacuation efficiency.
