Revenue management (RM) can be considered an application of operations research in the transportation industry. For these service companies, it is a difficult task to adjust supply and demand. In order to maximize revenue, RM systems display demand behavior by using historical data. Usually, parametric methods are applied to estimate the probability of choosing a product at a given time. However, parameter estimation becomes challenging when we need to deal with constrained data. In this research, we evaluate the performance of a revenue management system when a nonparametric method for choice probability estimation is chosen. The outcomes of this method have been compared to the total expected revenue using synthetic data.