###### Abstract  
Prolonged exposure to high-intensity noise environments in urban rail transit systems can negatively impact the health and work efficiency of drivers. However, there is a lack of comprehensive understanding of the noise pattern and, therefore, effective mitigation strategies. To control the noise in urban rail transit systems, this study proposes a comprehensive noise assessment framework, including metrics such as average sound pressure level, peak sound pressure level, percentile sound pressure levels, dynamic range, main frequency component, and cumulative time energy to evaluate the noise characteristics. We also employ a density-based spatial clustering of applications with noise (DBSCAN) method to identify the noise patterns with the evaluation of their hazard to urban rail transit drivers. The results have revealed that: (1) 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 intensity with substantial fluctuations. (2) Ten noise patterns were identified, with frequencies varying from 14.47 Hz to 69.70 Hz and Leq varying from 60 dB to 115 dB. (3) The major noise sources from these patterns are inferred to be the train's mechanical systems, wheel-rail interaction, aerodynamic effects, and braking systems. Combined with the noise patterns and urban rail transit's operation environment, this study proposes tailored mitigation strategies for applications aimed at protecting drivers' hearing health, enhancing work efficiency, and ensuring driving safety. urban rail transit; noise; human; environment; hazards; pattern identification; mitigation strategies +
Footnote †: journal:  
Urban rail transit noise predominantly originates from track-wheel friction, electrical system operations, and carriage structural resonance and generally converges in the driver's cab [14; 15]. Previous noise assessment research primarily utilizes sound level meters and spectrum analyzers to identify noise sources and characteristics through the measurement and analysis of noise intensity and spectral distribution [16; 17; 18]. Recent studies also incorporated numerical simulation techniques such as finite element analysis or computational fluid dynamics to accurately model the distribution of urban rail transit noise [19; 20]. However, the inadequate understanding of nuanced noise characteristics in existing urban rail transit noise control strategies leads to suboptimal mitigation effectiveness. Different noise sources usually exhibit distinct characteristics in their spectral distributions, yet existing control measures often fail to address these differences in a tailored manner. Specifically, [PERSON] et al. found that elastic mats and various noise barriers exhibit limitations in controlling wheel-rail noise [21]. [PERSON] et al. demonstrated that although the overall noise in air-conditioning ducts is managed, consequential noise issues persist at the front end of the cabin [22]. Furthermore, existing studies predominantly focus on controlling the sound pressure levels, with insufficient attention to the spectral characteristics of noise, including the specific impacts of different frequency components on human health [23]. Therefore, in-depth research into the detailed characteristics of urban rail transit noise and the development of more precise noise control strategies are essential to improving the effectiveness of noise control measures. To overcome the above shortcomings, we propose a _systematic noise control framework_ for urban rail transit driver cabs. Specifically, we proposed a set of noise evaluation metrics, employed density-based spatial clustering of applications with noise (DBSCAN) clustering algorithms for noise pattern identification and noise source derivation, and designed tailored control measures based on the identified noise patterns.