Passenger Congestion Alleviation in Large Hub Airport Ground Access System Based on Queueing Theory

TL;DR

This paper introduces a bi-level programming approach based on queueing theory to optimize airport ground access systems, reducing passenger congestion and improving evacuation efficiency through queue toll incentives.

Contribution

It develops a novel queueing network model and bi-level optimization framework incorporating passenger behavior and toll strategies to alleviate congestion at large hub airports.

Findings

The proposed method effectively reduces passenger queue lengths in simulations.

Queue toll incentives influence passenger mode choice to alleviate congestion.

The approach improves evacuation efficiency in both daytime and evening scenarios.

Abstract

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.