Relationship analysis between deterioration of switch support and railway passenger comfort using machine learning
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...
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| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
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| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/02/e3sconf_icome2025_01006.pdf |
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| author | Sresakoolchai Jessada Cheputeh Ni-Asri |
| author_facet | Sresakoolchai Jessada Cheputeh Ni-Asri |
| author_sort | Sresakoolchai Jessada |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5ffb9edc6082480c83db4900459fd525 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-5ffb9edc6082480c83db4900459fd5252025-01-16T11:22:34ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016020100610.1051/e3sconf/202560201006e3sconf_icome2025_01006Relationship analysis between deterioration of switch support and railway passenger comfort using machine learningSresakoolchai Jessada0Cheputeh Ni-Asri1Department of Civil and Environmental Engineering, Faculty of Engineering, Prince of Songkla UniversityDepartment of Mechanical and Mechatronics Engineering, Faculty of Engineering, Prince of Songkla UniversityPassenger 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/02/e3sconf_icome2025_01006.pdfpublic transportationrailway transportationrailway switchpassenger comfortmachine learning |
| spellingShingle | Sresakoolchai Jessada Cheputeh Ni-Asri Relationship analysis between deterioration of switch support and railway passenger comfort using machine learning E3S Web of Conferences public transportation railway transportation railway switch passenger comfort machine learning |
| title | Relationship analysis between deterioration of switch support and railway passenger comfort using machine learning |
| title_full | Relationship analysis between deterioration of switch support and railway passenger comfort using machine learning |
| title_fullStr | Relationship analysis between deterioration of switch support and railway passenger comfort using machine learning |
| title_full_unstemmed | Relationship analysis between deterioration of switch support and railway passenger comfort using machine learning |
| title_short | Relationship analysis between deterioration of switch support and railway passenger comfort using machine learning |
| title_sort | relationship analysis between deterioration of switch support and railway passenger comfort using machine learning |
| topic | public transportation railway transportation railway switch passenger comfort machine learning |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/02/e3sconf_icome2025_01006.pdf |
| work_keys_str_mv | AT sresakoolchaijessada relationshipanalysisbetweendeteriorationofswitchsupportandrailwaypassengercomfortusingmachinelearning AT cheputehniasri relationshipanalysisbetweendeteriorationofswitchsupportandrailwaypassengercomfortusingmachinelearning |