Multi-agent models of airline frequency competition for mitigating passenger delays

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  • Date/Time:
    • Tuesday January 18, 2011 - Wednesday January 19, 2011
      10:00 am - 10:59 am
  • Location: ISyE Executive classroom
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Summary Sentence: Multi-agent models of airline frequency competition for mitigating passenger delays

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TITLE:  Multi-agent models of airline frequency competition for mitigating passenger delays

SPEAKER:  Vikrant Vaze, Faculty Candidate in Transportation Systems


Airport congestion is imposing a tremendous cost on the world economy with demand often exceeding the capacity at the congested airports. Airline frequency competition is partially responsible for the growing demand for airport resources. Market share of an airline is a function of its frequency share. Based on the most commonly accepted form of this relationship, we propose a game-theoretic model of airline frequency competition. We prove the convergence of myopic best-response dynamics to a pure strategy Nash equilibrium. We provide an expression for the measure of inefficiency introduced by airline competition, similar to the price of anarchy, which is the ratio of the total cost of the worst-case equilibrium to the total cost of the cost minimizing solution.

Using actual data on air travel demand, costs and airfares, we obtain a lower bound on system-wide delays by solving a system-optimal problem. The solution to this large-scale mixed-integer programming problem shows that delays could be reduced substantially in the absence of competition. Next, we model airline frequency competition at a slot constrained airport and provide empirical validation of the Nash equilibrium outcome. A significant result shows that a small reduction in total number of allocated slots translates into a substantial reduction in congestion and delays, and also a considerable improvement in airlines’ profits.

Finally, lack of publicly available disaggregate passenger travel data has made it difficult to understand and quantify the impacts of congestion and congestion mitigation strategies on passenger delays and disruptions. We use multiple sources of publicly available data and develop a novel discrete choice-based approach to estimate the disaggregate passenger flow data. We quantify the passenger delay costs and provide insights into major factors affecting passenger delays.

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School of Industrial and Systems Engineering (ISYE)

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  • Created By: Anita Race
  • Workflow Status: Published
  • Created On: Jan 3, 2011 - 4:10am
  • Last Updated: Oct 7, 2016 - 9:53pm