Sitting comfort analysis and prediction for high-speed rail passengers based on statistical analysis and machine learning

Various comfort evaluation models have been extensively studied for improving driver seat and office seat designs for the purpose of improving seat comfort. High-speed rail (HSR) seat comfort is influenced by specific environments, activities, and postures, and targeted in-depth research on this topic is lacking. Existing evaluation cannot ensure the validity of data at the early stages of model training because of their direct reliance on subjective evaluation data, thereby limiting the improvement in the accuracy of prediction. In this study, we designed a sitting observation experiment and static sitting comfort experiment for the typical activities of HSR passengers. The sitting posture of HSR passengers under typical activities was classified for different comfort levels based on the factors influencing the sitting posture in terms of bone and muscle biomechanics. The correlations between the subjective discomfort in different sitting positions, objective change patterns, and body pressure distribution parameters were statistically analysed. The possibility of using sitting duration and sitting frequency as objective behavioural indicators of discomfort evaluation was indicated. Based on the verification of the reliability of the evaluation data, the classification performance of various machine learning algorithms was compared and analysed, and a sitting comfort prediction model was established based on the gradient boosting machine algorithm, with an accuracy of 89.5%. This study serves as an effective reference for improvement strategies for HSR passenger comfort and will inspire new ideas for exploring objective indicators for sitting comfort evaluation. • Measured sitting behaviour and body pressure data of high-speed rail passengers. • Collected Subjective discomfort scores for the typical sitting posture. • Analysed statistical relationships between discomfort and behaviour and pressure. • Indicated behavioural indicators to verify the validity of subjective ratings. • Developed a machine learning model for predicting passengers sitting discomfort.