Big data method more accurately predicts air traveler choices

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Dr. Ahmed Abdelghany developed a new way to calibrate computer models that predict travelers’ choices, while considering capacity-constrained demand – an innovation that overcomes the limitations of current practice. His new methodology could help airlines accurately predict passenger demand, which drives scheduling, fleet assignment and revenue management decisions.
How does it work. Abdelghany’s approach is a simulation-based tool for calibrating passenger itinerary choice models. His algorithm simulates air travelers versus all available itineraries, running through many iterations. At each iteration, the algorithm learns to minimize any discrepancies between the simulation results and data describing historical demand.

Collected and summarized from the source below by Tran Thi Tan  https://www.traveldailynews.com/post/big-data-method-more-accurately-predicts-air-traveler-choices