In this study, a data-driven interior noise prediction model is developed for vehicles on an urban rail transit system based on random forest (RF) and a vehicle/track coupling dynamic model (VTCDM). The proposed prediction model can evaluate and optimize the sustainability of railway alignment from the perspective of interior noise. First, a data collection framework via embedded sensors of onboard smartphones was developed. Then, for establishing the mapping relationship between the dynamic responses of the car body and interior noise, the collected dataset was fed to the RF. Parameter, error distribution, and feature importance analyses were conducted for evaluating and optimizing the performance of the RF. With the optimized parameters, the probability of prediction errors being within 5 dB was 86.9%. Next, the VTCDM was established using an existing industry multibody simulation tool and verified through a comparison between the simulated and field dynamic responses. Finally, a case study that extends the application of this interior noise prediction model to railway alignment design is presented.

中文翻译：

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城市轨道交通车辆内部噪声的预测及其在铁路线形设计中的应用

在这项研究中，基于随机森林（RF）和车辆/轨道耦合动态模型（VTCDM），为城市轨道交通系统上的车辆开发了数据驱动的内部噪声预测模型。所提出的预测模型可以从内部噪声的角度评估和优化铁路路线的可持续性。首先，通过车载智能手机的嵌入式传感器开发了数据收集框架。然后，为了建立车身动态响应与内部噪声之间的映射关系，将收集的数据集馈入RF。进行了参数，误差分布和特征重要性分析，以评估和优化RF性能。使用优化的参数，预测误差在5 dB以内的概率为86.9％。下一个，VTCDM是使用现有的行业多体仿真工具建立的，并通过仿真和现场动态响应之间的比较进行了验证。最后，提出了一个案例研究，将这种内部噪声预测模型的应用扩展到铁路线形设计中。