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Train-induced ground-borne vibration has a negative effect on residential areas near railway tracks. Residents who are regularly exposed to ground-borne vibration can experience sleep disturbances and more serious health problems in the long run. In addition, it concerns the mental health of those who live nearby. Residents’ productivity and quality of life can be harmed as a result of direct exposure to train-induced ground-borne vibration. The relevant authorities must record a few precise measurements using technically sophisticated instruments and equipment to research further the impact of ground-borne vibrations induced by train traffic. However, the equipment is usually costly, and it has become one of the main stumbling blocks to achieving the desired results. This paper aimed to propose an alternative to the authority’s current guidelines and standards for vibration limits and environmental control. This research established a regression prediction model to forecast the peak particle velocity of commuter train ground-borne vibration. The established model considered a few parameters obtained from site surveys with limited or no tools at all. The data collected was measured along the ground rail tracks involving human-operated trains. Residents living in landed residential areas near railway tracks were selected as the recipients. Finally, the peak particle velocity models were established, validated, and a sensitivity analysis was carried out.

Developing an empirical model that can predict ground-borne vibration is required in the modelling process using actual data of ground vibration velocity induced by train traffic collected from sites. In the preliminary and mitigation planning stages of the project, the empirical models developed are expected to predict the ground-borne vibration velocity due to rail traffic. The findings of this research are expected to provide a new perspective for railway planners and designers to improve the national design to improve the quality of life for the residents living close to the rail tracks. This research study firmly fills the information gap towards a fundamental understanding of ground-borne vibration in numerous areas of learning regarding the condition of train operation. This study has developed a prediction model of regression to forecast the peak particle velocity of ground-borne vibration from freight trains based on correlated and fixed parameters. The models developed have considered a few parameters obtained from sites using minimal or without tools altogether. Speed of trains and distance of receivers from the sources were the only significant parameters found in this study and used to simplify the empirical model. Type of soil, which is soft soil, and type of train, which is freight train, were the fixed parameters for this study. The data collected were measured along the ground rail tracks involving human-operated freight trains. Residents from the landed residential areas near the railway tracks were chosen as the receivers. Finally, the peak particle velocity models have been successfully developed, and validation analysis was conducted. The model can be used by authorities in the upcoming plan for the new rail routes based on similar fixed parameters with correlated parameters from the study.

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