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.
|Title of host publication||KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining|
|Publisher||Association for Computing Machinery|
|Number of pages||11|
|Publication status||Published - 2021 Aug 14|
|Event||27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore|
Duration: 2021 Aug 14 → 2021 Aug 18
|Name||Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Conference||27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021|
|Period||21/8/14 → 21/8/18|
Bibliographical noteFunding Information:
Noseong Park is the corresponding author. This work of Jinsung Jeon, Seoyoung Hong, and Noseong Park was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)). The work of Thai Le and Dongwon Lee was in part supported by NSF awards #1909702, #1940076, #1934782, and #2114824.
© 2021 ACM.
All Science Journal Classification (ASJC) codes
- Information Systems