2020
Thekinen, J; Moolchandani, K; Panchal, J; DeLaurentis, D
Modeling Airlines’ Route Selection Decisions Under Competition: A Discrete-Games-Based Model Journal Article
In: Journal of Air Transportation, 28 (1), pp. 3-14, 2020.
Abstract | BibTeX | Tags: Complex Networked Systems, Game Theory | Links:
@article{thekinen2020modeling,
title = {Modeling Airlines’ Route Selection Decisions Under Competition: A Discrete-Games-Based Model},
author = {Thekinen, J and Moolchandani, K and Panchal, J and DeLaurentis, D},
url = {https://josephdthekinen.com/wp-content/uploads/2021/11/2018_JAT_Modeling-Airlines-Route-Selection-Decisions-Under-Competition-A-Discrete-Games-Based-Model.pdf},
doi = {10.2514/1.D0153},
year = {2020},
date = {2020-02-01},
urldate = {2020-02-01},
journal = {Journal of Air Transportation},
volume = {28},
number = {1},
pages = {3-14},
abstract = {To analyze the effects of policies within the air transportation network, there is a need to model how policies affect the decisions made by airlines. Because airline decision making is based on proprietary information, such models need to rely on openly available data sources. In this paper, openly available data from the Bureau of Transportation Statistics are used to develop a predictive model of airline route selection decisions. The proposed model accounts for airline competition and models parameters such as operating cost, which can be influenced by policymakers. This paper illustrates the model using a dataset from two major airlines in U.S. domestic air transportation network. The dataset and the cost model are used for Bayesian estimation of model parameters, which are then used to predict the effects of cost and demand on the evolution of the network topology. The proposed model is found to be more accurate than competing models that do not consider the competition. From the estimates obtained on preference parameters, it is found that decreasing the operating cost and increasing the market demand increase the probability of operating service on the route for airlines, and the operating cost has a greater effect than market demand and route distance on the route selection decisions.},
keywords = {Complex Networked Systems, Game Theory},
pubstate = {published},
tppubtype = {article}
}
To analyze the effects of policies within the air transportation network, there is a need to model how policies affect the decisions made by airlines. Because airline decision making is based on proprietary information, such models need to rely on openly available data sources. In this paper, openly available data from the Bureau of Transportation Statistics are used to develop a predictive model of airline route selection decisions. The proposed model accounts for airline competition and models parameters such as operating cost, which can be influenced by policymakers. This paper illustrates the model using a dataset from two major airlines in U.S. domestic air transportation network. The dataset and the cost model are used for Bayesian estimation of model parameters, which are then used to predict the effects of cost and demand on the evolution of the network topology. The proposed model is found to be more accurate than competing models that do not consider the competition. From the estimates obtained on preference parameters, it is found that decreasing the operating cost and increasing the market demand increase the probability of operating service on the route for airlines, and the operating cost has a greater effect than market demand and route distance on the route selection decisions.