  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.
