Train Passengers Prediction for Railway Management System Using Regression Analysis

Passenger predicting is an integral component of advanced, automated, and innovative railway transit operations and management. In this paper, regression analysis was utilized using the Polynomial regression method to predict the passengers of Philippine railway transportation during the COVID-19 pandemic and in the succeeding years 2022, 2023, and 2024 for the main railway transits in the country namely: Light Rail Transit Line 1 (LRT-1), Light Rail Transit Line 2 (LRT-2), and Metro Rail Transit Line 3. Polynomial regression is used when the data has a non-linear relationship with each other and the independent variables have to be modified to some degree. During the pandemic in 2020, the train passengers were decreased by 68.38% (LRT1), 78.06% (LRT2), and 67.19% (MRT3). The predicted number of train passengers is starting to increase in 2022 from 22% to 30%. The model formulation with the utilization of regression analysis is an effective way for passenger volume prediction that can be used for railway management systems such as rail transport service evaluation, train passenger crowd regulation, and train scheduling system.