Relationship analysis between deterioration of switch support and railway passenger comfort using machine learning

¹ Department of Civil and Environmental Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, Thailand
² Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, Thailand

^* Jessada Sresakoolchai: Jessada.sr@psu.ac.th

Abstract

Passenger comfort is one of the critical factors in the railway system. Passenger comfort plays an important role in the success and effectiveness of railway transportation systems in terms of passenger satisfaction, health and well-being, economy, competition capability, and safety. For the railway infrastructure, one of the important components is a switch. Switches in the railway system have the function of guiding rolling stocks or trains to the preferred directions and tracks. However, switches are the components of the railway infrastructure that can negatively affect railway passenger comfort due to their geometry and the functions that are used to change the direction of rolling stocks. This study aims to study the relationship between the deterioration of railway switch support and railway passenger comfort by using machine learning. The data used in the study are axle box accelerations that are numerically simulated from verified multi-body simulation models. The machine learning technique used in the study is the convolutional neural network. The indicator used to evaluate the machine learning model’s performance is the accuracy. From the machine learning model development and training, the accuracy of the machine learning model is higher than 80% which is satisfied. Railway operators can benefit from the study’s findings by applying the developed machine learning model to collect data to evaluate the deterioration of railway switch support and the effect on railway passenger comfort.