Passenger comfort prediction via time-series classification.

The seat comfort experience during a flight is one of the most crucial factor impacting the passenger’s intention of flying with the same airline in future occasions, therefore an effective strategy to increase passenger satisfaction cannot be separated from improving the comfort of the seat. Due to its cost-effectiveness, the analysis of the pressure distribution at the seat-occupant interface is one of the most widely used methods in the diagnostic evaluation of seat (dis-)comfort. Dealing with the research issue of predicting subjective (dis-)comfort via seat pressure measurements, this study aims to identify the seat pressure index that best predicts seat (dis-)comfort by investigating five different pressure indexes via eleven time series classification algorithms.