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arXiv · 2105.15012

Large-Scale Data-Driven Airline Market Influence Maximization

Duanshun Li·Jing Liu·Jinsung Jeon·Seoyoung Hong·Thai Le·Dongwon Lee·Noseong Park
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Abstract

We present a prediction-driven optimization framework to maximize the market influence in the US domestic air passenger transportation market by adjusting flight frequencies. At the lower level, our neural networks consider a wide variety of features, such as classical air carrier performance features and transportation network features, to predict the market influence. On top of the prediction models, we define a budget-constrained flight frequency optimization problem to maximize the market influence over 2,262 routes. This problem falls into the category of the non-linear optimization problem, which cannot be solved exactly by conventional methods. To this end, we present a novel adaptive gradient ascent (AGA) method. Our prediction models show two to eleven times better accuracy in terms of the median root-mean-square error (RMSE) over baselines. In addition, our AGA optimization method runs 690 times faster with a better optimization result (in one of our largest scale experiments) than a greedy algorithm.

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Duanshun Li, Jing Liu, Jinsung Jeon, Seoyoung Hong, Thai Le, Dongwon Lee, Noseong Park. 2021-05-31. Large-Scale Data-Driven Airline Market Influence Maximization. https://arxiv.org/abs/2105.15012

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