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Massachusetts Institute of Technology

Competition and congestion in the National Aviation System : multi-agent, multi-stakeholder approaches for evaluation and mitigation

Abstract

dc:description.abstract

The US National Aviation System (NAS) is a complex system with multiple, interacting agents including airlines, passengers, and system operators, each with somewhat different objectives and incentives. These interactions determine the state of the system. NAS congestion and delays result in additional operating costs and reduced profitability for the airlines, a decrease in the level-of-service to passengers, and a decrease in the efficiency of NAS resource utilization. We evaluate the congestion impacts on the NAS stakeholders while explicitly accounting for their interactions and propose congestion mitigation mechanisms that are beneficial to these different stakeholders. We measure the extent to which the NAS capacity is being inefficiently utilized. We show that at the current level of passenger demand, delays are avoidable to a large extent if we control the negative effects of competitive airline scheduling practices, thus providing critical insights into the nature and causes of delays. We develop a detailed framework using data fusion and discrete choice modeling' for generating disaggregate passenger travel data. We characterize the impacts of airline network structures, schedules and operational decisions on passenger delays. We propose a parametric game-theoretic model consistent with the most popular characterization of frequency competition. We prove that the level of congestion in a system of competing airlines is an increasing function of 1) the number of competing airlines, 2) a measure of the gross profit margin, and 3) the frequency sensitivity of passenger demand. We propose a game-theoretic model of frequency competition under slot constraints and provide empirical and algorithmic justifications of the suitability of the Nash equilibrium solution concept for modeling these games. We devise and assess new administrative strategies for congestion mitigation. We show that a small reduction in the total number of allocated slots translates into a substantial reduction in delays, and also a considerable improvement in airlines' profits. We develop an equilibrium model of frequency competition in the presence of delay costs and congestion prices. We find that the success of congestion pricing critically depends on the characteristics of frequency competition in individual markets. We also identify critical differences between flat pricing and marginal cost pricing. Key words: Airline Scheduling, Airline Frequency Competition, National Aviation System, Stakeholders, Multi-agent Models, Nash Equilibrium, Game Theory, Price of Anarchy, Passenger Delays, Cancellations, Missed Connections, Cost of Passenger Disruptions, Administrative Slot Controls, Slot Reduction, Congestion Pricing.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2011

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Vaze, Vikrant (Vikrant Suhas)
Advisor dc:contributor.advisor
  • Cynthia Barnhart.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/70756
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/70756

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Vaze, Vikrant (Vikrant Suhas). Competition and congestion in the National Aviation System : multi-agent, multi-stakeholder approaches for evaluation and mitigation. Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/70756