Publications -> Conference Papers

MAP: Frequency-Based Maximization of Airline Profits based on an Ensemble Forecasting Approach


Authors: B. An, H. Chen, N. Park, and V. S. Subrahmanian
Title: MAP: Frequency-Based Maximization of Airline Profits based on an Ensemble Forecasting Approach
Abstract: 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 state-of-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 MAP-Greedy (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).
Keywords: Ensemble prediction; Regression; Airline demand and market share prediction; Airline profit maximization
Conference Name: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'16)
Location: San Francisco, USA
Publisher: ACM
Year: 2016
Accepted PDF File: MAP_Frequency-Based_Maximization_of_Airline_Profits_based_on_an_Ensemble_Forecasting_Approach_accepted.pdf
Permanent Link: http://dx.doi.org/10.1145/2939672.2939726
Reference: B. An, H. Chen, N. Park, and V. S. Subrahmanian, “MAP: Frequency-based maximization of airline profits based on an ensemble forecasting approach,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16). ACM, August 2016, pp. 421–430.
bibtex: 
@inproceedings{LILY-c97, 
    author	= {An, Bo and Chen, Haipeng and Park, Noseong and Subrahmanian, V. S.},
    title	= {{MAP}: Frequency-Based Maximization of Airline Profits based on an Ensemble Forecasting Approach},  
    booktitle	= {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'16)}, 
    year		= {2016}, 
    month	= {August}, 
    pages	= {421-430}, 
    location	= {San Francisco, USA},
    publisher	= {ACM},
 }