A DYNAMIC PRICING FRAMEWORK FOR TRAIN TICKETS USING MACHINE LEARNING PREDICTION AND RULE-BASED ADAPTATION

The railway transportation system in Indonesia has experienced a significant increase in demand, especially during holiday seasons, indicating the need for ticket price optimization to maximize revenue and balance passenger distribution. This study aims to develop a simple and efficient dynamic pricing model for train tickets, addressing the issues of static pricing and the confusing complexity of ticket subclasses for passengers and management. The methods employed include identifying key factors influencing ticket prices (booking time, route, service type, demand) and building a robust price prediction model using the XGBoost algorithm. Train ticket purchase transaction data from 2020 to 2025, including details like purchase time, route, ticket class, and schedule popularity, were utilized to generate accurate base prices. These base prices are then adjusted in real-time considering current demand and seat availability. Dynamic pricing simulations will evaluate price increases based on demand percentage and train occupancy rates. Model evaluation will use R-squared (R²), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics to measure prediction accuracy. The results of this study are expected to contribute significantly to railway companies in optimizing ticket pricing strategies and improving operational efficiency.