Using a Machine Learning Approach to Predict the Thailand Underground Train’s Passenger

Abstract

In today’s world, data has become an asset for businesses. Many sectors use data technology to advance their businesses. Building management is one of the processes on which numerous studies have been conducted to assist building users. Thailand has progressed in terms of transportation infrastructure and public transportation. The Metropolitan Rapid Transit (MRT) system has more than one hundred million users per year. However, crowding is a concern in the present since crowding creates a problem and reduces customer pleasure. The goal of this research is to create a machine learning model for forecasting passenger demand over time. In addition, standard data collecting equipment was used to collect data from the Metropolitan Rapid Transit (MRT) Purple Line. This line has a total of 16 stations. Station name, date, day, month, period, number of passengers, holidays, weekends, and weather are among the nine factors. Analysis approaches included the analysis phase, classification, and regression algorithm. However, the regression algorithm’s accuracy is poor and therefore cannot be used. Before using machine learning classification methods, the K-means was used to cluster the types of passengers. In addition, for this investigation, three classification methods were used: artificial neural network, random forest, and decision tree. Furthermore, the findings revealed that the artificial neural network has a high predicting accuracy. The accuracy value stated is more than 0.85 for demand over time.