Though there are numerous traditional models to predict market share and demand along airline routes, the prediction of existing models is not precise enough and, to the best of our knowledge, there is no use of data-mining based forecasting techniques to improve airline profitability. We propose the MAP (Maximizing Airline Profits) architecture designed to help airlines and make two key contributions in airline market share and route demand prediction and prediction-based airline profit optimization. Compared with past methods to forecast market share and demand along airline routes, we introduce a novel Ensemble Forecasting (MAP-EF) approach considering two new classes of features: (i) features derived from clusters of similar routes, and (ii) features based on equilibrium pricing. We show that MAP-EF achieves much better Pearson Correlation Coefficients (over 0.95 vs. 0.82 for market share, 0.98 vs. 0.77 for demand) and R2-values compared with three stateof- the-art works for forecasting market share and demand, while showing much lower variance. Using the results of MAP-EF, we develop MAP-Bilevel Branch and Bound (MAP-BBB) and MAPGreedy (MAP-G) algorithms to optimally allocate flight frequencies over multiple routes, to maximize an airline's profit. Experimental results show that airlines can increase profits by a significant margin. All experiments were conducted with data aggregated from four sources: US Bureau of Transportation Statistics (BTS), US Bureau of Economic Analysis (BEA), the National Transportation Safety Board (NTSB), and the US Census Bureau (CB).
|Title of host publication||KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|Publication status||Published - 2016 Aug 13|
|Event||22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States|
Duration: 2016 Aug 13 → 2016 Aug 17
|Name||Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Conference||22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016|
|Period||16/8/13 → 16/8/17|
Bibliographical notePublisher Copyright:
© 2016 ACM.
All Science Journal Classification (ASJC) codes
- Information Systems