| Popis: |
This article proposes a data-driven ticket dynamic pricing methodology for passenger railway service providers. There is a finite purchasing horizon, and the ticket prices should be set under varying conditions to affect the customer booking behaviour. A three-step process including machine learning and optimization tools is employed to maximize the revenue under a constrained train capacity. First, a multi-layer perceptron artificial neural network (MLP-ANN) model is proposed to predict the demand intensity due to seasonal situations using the ticket reservation data. Then, some regression models as price elasticity functions are used to quantify the effects of price, seasonal conditions and competition on the company’s sales. Finally, a nonlinear integer programming model is proposed to maximize the total revenue in the purchasing horizon. The results of the numerical studies on the Fadak Five-Star Trains’ reservation data indicate that the proposed methodology has high-grade potential to improve the service provider’s revenue. |
