Endogenous control of service rates in stochastic and dynamic queuing models of airport congestion

Abstract

Airport congestion mitigation requires reliable delay estimates. This paper presents an integrated model of airport congestion that combines a tactical model of capacity utilization into a strategic queuing model. The model quantifies the relationships between flight schedules, airport capacity and flight delays, while accounting for the way arrival and departure service rates can be controlled over the day to maximize operating efficiency. We show that the model estimates well the average and variability of the delays observed at New York's airports. Results suggest that delays can be extremely sensitive to even small changes in flight schedules or airport capacity.

Capacity Building through Efficient Use of Existing Airport Infrastructure

“…For example, Barnhart et al (2012) define capacity as the number of movements per hour; Uphamet al 2003, and Graham and Guyer (1999) -as a function of operational and environmental constraints. More recently academic research on airport operations tends to focus on the relationship between flight schedules, airport capacity and how to mitigate delays (Jacquillat and Odoni, 2015b;Zografos et al, 2017). Mujica et al 2017propose defining airport capacity as a function of airline and airport business models, airport infrastructure, regulations, and capacity caps imposed by the government for environmental or society-related reasons.…”

Performance Analysis of Time-Dependent Queueing Systems: Survey and Classification

“…In the analysis, the runway's service rate depends on a second queue representing the accumulated snow at airports. Jacquillat and Odoni [104] use a queueing model in their algorithm to control departure and arrival service rates to maximize the efficiency of an airport's runway system. Jung and Lee [112] propose a dynamic programming approach with an embedded time-dependent queueing model to staff air traffic controllers.…”

“…Galliher and Wheeler [75] x D T A Koopman [133] x CKE, DTA, FLUID Omosigho and Worthington [176] x D T A Bookbinder [21] x CKE Jung and Lee [112] x CKE Daniel [49] x x DTA Hebert and Dietz [98] x BOT Daniel and Pahwa [52] x x DTA Janic [106] x x DIFF Daniel and Harback [50] x x DTA Stolletz [203] SBC Daniel and Harback [51] x x DTA Janic [107] x FLUID Swaroop et al [206] x x FLUID Lovell et al [146] x DIFF Jacquillat and Odoni [104] x x CKE private line telecommunication services by using the explicit solution of a GðtÞ XðtÞ =GðtÞ=1 system.…”

Performance Comparing and Analysis for Slot Allocation Model

“…The need for an immediate relief to seriously congested airports calls for short to medium-term, demand-side solutions that are based on the optimum allocation and use of available airport capacity [5]. To control over-capacity scheduling, the most common demanding management schemes fall into two categories: (i) approaches introducing market-driven or pure economic instruments (e.g., slot trading, auctions, congestion pricing), which aim to allocate capacity among competing users by considering real market (or approximations of) valuations of access to congested airport facilities [6][7][8][9][10][11][12]; (ii) efforts aiming to improve the efficiency by using administrative allocation mechanism [2][3][4][5][13][14][15][16][17][18][19].…”