Large-Scale Data-Driven Airline Market Influence Maximization

TL;DR

This paper introduces a prediction-driven optimization framework using neural networks and a novel adaptive gradient ascent method to maximize airline market influence by adjusting flight frequencies across thousands of routes.

Contribution

It develops a new large-scale, data-driven approach combining neural network predictions with an innovative optimization algorithm for airline influence maximization.

Findings

Prediction models improve accuracy by 2-11 times over baselines.

The AGA method is 690 times faster than greedy algorithms.

The framework effectively maximizes market influence across 2,262 routes.

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.