# Large-Scale Data-Driven Airline Market Influence Maximization

arXiv: https://arxiv.org/abs/2105.15012 · HTML: https://mlsanity.com/paper/2105.15012/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 30 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 1 of 1)

- [Capacitated Dynamic Programming: Faster Knapsack and Graph Algorithms](https://mlsanity.com/paper/1802.06440/) (2018)

## Built on by (most cited first, 2 of 2)

- [Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/2309.02669/) (2023)
- [Prediction-based One-shot Dynamic Parking Pricing](https://mlsanity.com/paper/2208.14231/) (2022)

## Abstract (arXiv)

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.