## 6 Conclusions  
Urban rail transit is an essential component for advancing sustainable urban transportation [51; 52]. The implementation of proactive noise mitigation measures in urban rail transit is essential for ensuring operational safety and enhancing environmental sustainability [53; 54; 55]. In this study, we propose a systematic noise control framework for urban rail transit driver cabs, propose detailed noise evaluation metrics, identify the noise pattern based on the DBSCAN algorithm, infer the potential noise source, and recommend several noise control strategies tailored to the characteristics of different noise sources. The application value of this approach was validated through field experiments conducted in the driver's cab of Lanzhou Urban Rail Transit Line 1.  
The primary findings of this study include the following aspects: First, the equivalent continuous sound pressure level (Leq) in the cab of Lanzhou Urban Rail Transit Line 1 averages 87.12 dB with a standard deviation of 8.52 dB, which reveals a high noise in-tensity with substantial fluctuations, which pose a risk of hearing damage to the drivers. Second, we identified ten distinct noise patterns, which can be categorized into four main types: low-frequency high-intensity noise, high-frequency high-intensity noise, low-frequency low-intensity noise, and high-frequency low-intensity noise. The primary sources of these noise types are inferred to be the train's mechanical system, wheel-rail contact, aerodynamic effects, and braking system, respectively. Third, we proposed comprehensive noise control strategies, including optimizing the train's deceleration process, installing vibration dampers and elastic fasteners, regularly grinding the rails, and covering air conditioning and certain equipment with sound-absorbing materials.  
This study also has several limitations. Specifically, the data primarily come from Lanzhou Urban Rail Transit Line 1, which may have regional limitations, as the noise characteristics and sources in urban rail transit systems of other cities are diverse. Therefore, future research should expand the dataset to contain more cities and multiple types of urban rail transit lines to validate the generalizability of the proposed noise control framework. Additionally, while the clustering methodology provides a simple and cost-effective means of identifying noise sources in the metro driver's cabin, it has inherent limitations regarding the precision of noise source analysis. This approach relies on inferred associations between observed noise patterns and noise sources in literature, which may not capture all of the variability present in real-world conditions. For a more precise understanding of the contribution of various noise sources, installing sensors at different positions within the train body is necessary. Such an arrangement allows for detailed spatial analysis and accurate identification of specific noise sources, thereby enhancing the reliability of the noise assessment. Lastly, the noise control strategies in this study mainly focus on short-term effects and lack an assessment of long-term consequences. Future studies should investigate the long-term stability and adaptability of noise control measures, especially under different environmental conditions and operational states.  
Conceptualization, Z.S. and B.Y.; Methodology, C.Y.; Software, L.H.; Validation, Z.S. and B.Y.; Formal analysis, L.H.; Investigation, L.H.; Resources, Z.S.; Writing--original draft, B.Y.; Writing--review & editing, C.Y.; Visualization, Y.Z.; Project administration, B.Y.; Funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
Dual-carbon Research Center of Hangzhou City University [Grant No.: 2023 ST04].  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available to prevent the disclosure of metro operational information, thereby ensuring its operation security.