Research on data-driven evaluation of passenger comfort

Xin Liu; Xiaoqing Li

doi:10.1117/12.3007084

25 October 2023 Research on data-driven evaluation of passenger comfort

Xin Liu, Xiaoqing Li

Proceedings Volume 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023); 1280168 (2023) https://doi.org/10.1117/12.3007084
Event: Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 2023, Dalian, China

Abstract

This paper focuses on the evaluation of passenger comfort in high-speed maglev trains using a data-driven approach. Specifically, the study targets the suspension control system as the object of research. The process begins with preprocessing the vibration acceleration data. Next, five-dimensional features are extracted, and the Jarque-Bera method is employed to test for normal distribution. If the feature values do not conform to a normal distribution, the Box-Cox transformation is applied. Finally, the features are standardized using the Z-score method, and the Euclidean distance is calculated based on a multi-feature fusion approach using hypersphere. The resulting Euclidean distance, referred to as the τ distance, serves as an evaluation metric for passenger comfort. Comparative experiments using real-world data demonstrate that this metric effectively assesses passenger comfort. The findings of this study have practical implications for evaluating the performance of suspension control systems and can be utilized to improve the design and operation of high-speed maglev trains, thereby enhancing the overall passenger experience.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xin Liu and Xiaoqing Li "Research on data-driven evaluation of passenger comfort", Proc. SPIE 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 1280168 (25 October 2023); https://doi.org/10.1117/12.3007084

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KEYWORDS

Education and training

Statistical analysis

Vibration

Histograms

Feature extraction

Control systems

Reflection

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