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.
2016
Moolchandani, K; Sha, Z; Maheshwari, A; Thekinen, J; Davendralingam, N; Panchal, J; DeLaurentis, D
Towards A Hierarchical Decision-Centric Modeling Framework for Air Transportation Systems Conference
16th AIAA Aviation Technology, Integration, and Operations Conference, Washington, D.C., 2016.
Abstract | BibTeX | Tags: Complex Networked Systems | Links:
@conference{moolchandani2016hierarchical,
title = {Towards A Hierarchical Decision-Centric Modeling Framework for Air Transportation Systems},
author = {Moolchandani, K and Sha, Z and Maheshwari, A and Thekinen, J and Davendralingam, N and Panchal, J and DeLaurentis, D},
url = {https://josephdthekinen.com/wp-content/uploads/2021/11/2016_AIAA_Towards-A-Hierarchical-Decision-Centric-Modeling-Framework-for-Air-Transportation-Systems.pdf},
year = {2016},
date = {2016-06-04},
booktitle = {16th AIAA Aviation Technology, Integration, and Operations Conference, Washington, D.C.},
pages = {3154},
abstract = {This work is part of ongoing research towards the development of a multi-level framework that is envisioned to provide the ability to holistically study the future evolution of the ATS as a result of decisions made by relevant stakeholders. In prior work, the authors have developed a discrete choice model to mimic the airlines route-planning decisions. In this paper, we present a passengers choice model, which forms the post-airline decision phase in the resulting bi-level decision-making framework. The passengers in our model make decisions on the type of itineraries being offers by the airlines, including both the choice of number of layovers to make and where to make those layovers. Specifically, we show separate multiple linear regression models for passengers choices on three different types of itineraries non-stop, one-stop, and two-or-more-stops. We present results of our model using data from the Bureau of Transportation Statistics.},
keywords = {Complex Networked Systems},
pubstate = {published},
tppubtype = {conference}
}
This work is part of ongoing research towards the development of a multi-level framework that is envisioned to provide the ability to holistically study the future evolution of the ATS as a result of decisions made by relevant stakeholders. In prior work, the authors have developed a discrete choice model to mimic the airlines route-planning decisions. In this paper, we present a passengers choice model, which forms the post-airline decision phase in the resulting bi-level decision-making framework. The passengers in our model make decisions on the type of itineraries being offers by the airlines, including both the choice of number of layovers to make and where to make those layovers. Specifically, we show separate multiple linear regression models for passengers choices on three different types of itineraries non-stop, one-stop, and two-or-more-stops. We present results of our model using data from the Bureau of Transportation Statistics.
2015
Sha, Z; Moolchandani, K; Maheshwari, A; Thekinen, J; Panchal, J; DeLaurentis, D
Modeling Airline Decisions on Route Planning Using Discrete Choice Models Conference
15th AIAA Aviation Technology, Integration, and Operations Conference, Dallas - Texas, 2015.
Abstract | BibTeX | Tags: Complex Networked Systems | Links:
@conference{sha2015modeling,
title = {Modeling Airline Decisions on Route Planning Using Discrete Choice Models},
author = {Sha, Z and Moolchandani, K and Maheshwari, A and Thekinen, J and Panchal, J and DeLaurentis, D},
url = {https://josephdthekinen.com/wp-content/uploads/2021/11/2015_Sha_Modeling-Airline-Decisions-on-Route-Planning-Using-Discrete-Choice-Models.pdf},
year = {2015},
date = {2015-06-22},
urldate = {2015-06-22},
booktitle = {15th AIAA Aviation Technology, Integration, and Operations Conference, Dallas - Texas},
pages = {2438},
abstract = {We propose a model for the airlines’ decisions on route planning, i.e., the decision on selecting which route to add and delete, using discrete choice random-utility theory. The central hypothesis is that a discrete choice model can effectively model the airlines’ decisions on route selection , and thereby help model the evolution of the air transportation network. We first model the airlines’ utility function as a linear function of decision variables with interaction effects. The decision of route selection is then modeled using a binary choice model derived from the utility function. The preferences for each variable in the utility function are estimated using historical datasets. Advantages of this approach include the ability to use statistical techniques to quantitatively construct decision models as well as to account for the uncertainty in unobserved attributes of the decision model. The proposed model helps predict the airlines’ decisions on routes addition and deletion which affect the network topology of air transportation and its future evolution. This capability can be beneficial to other stakeholders, such as Federal Aviation Administration, who may need to make their decisions in response to those made by the airlines, but do not have access to the airlines’ true decision models.},
keywords = {Complex Networked Systems},
pubstate = {published},
tppubtype = {conference}
}
We propose a model for the airlines’ decisions on route planning, i.e., the decision on selecting which route to add and delete, using discrete choice random-utility theory. The central hypothesis is that a discrete choice model can effectively model the airlines’ decisions on route selection , and thereby help model the evolution of the air transportation network. We first model the airlines’ utility function as a linear function of decision variables with interaction effects. The decision of route selection is then modeled using a binary choice model derived from the utility function. The preferences for each variable in the utility function are estimated using historical datasets. Advantages of this approach include the ability to use statistical techniques to quantitatively construct decision models as well as to account for the uncertainty in unobserved attributes of the decision model. The proposed model helps predict the airlines’ decisions on routes addition and deletion which affect the network topology of air transportation and its future evolution. This capability can be beneficial to other stakeholders, such as Federal Aviation Administration, who may need to make their decisions in response to those made by the airlines, but do not have access to the airlines’ true decision models.