## 4 Results Analysis  
### Noise Data Collection and Assessment  
Lanzhou Rail Transit Line 1 is the first operational urban rail transit line in Lanzhou, Gansu Province, China, with a total length of 25.9 km. This study chose the driver's cab of Lanzhou Rail Transit Line 1 as the case for detailed analysis. The sound measurement equipment used in this study includes the Sound Level Meter 2270-S, Microphone Bruel & Kjaer 4189-A-021, and Acoustic Calibrator 4231, all manufactured by Bruel & Kjaer in Naerum, Denmark. Additionally, the Cloud Intelligence Acquisition System INV3062A is produced by Beijing Leike Technology Co., Ltd. in Beijing, China. The equipment is illustrated in Figure 2. To ensure the collected noise data accurately reflect what the driver hears, the equipment was installed at a height of 1.2 m, aligning with the driver's ear level during normal operation, as demonstrated in Figure 3.  
Based on the collected original noise data, the fast Fourier transform (FFT) was applied to convert the data into the frequency domain. Following this, a band-stop filter was utilized to remove interference within specific frequency ranges, thereby reducing background noise. Figure 4 presents a comparison of the noise signals before and after filtering. The results demonstrate that the filtering process effectively minimizes background noise interference in non-essential intervals (such as regions 1 and 3) while preserving significant information during essential intervals where high-noise events occur (regions 2 and 4). This method enhances the extraction of key information from the noise signal. Figure 3: Position of noise collection equipment: (**a**) floor plan; (**b**) actual position. Figure 2: Noise collection equipment: (**a**) sound level meter; (**b**) free-field microphone; (**c**) acoustic calibrator. Subsequent to the filtering, the hourly sound pressure level (SPL) of the whole day was calculated, as shown in Figure 5. The data illustrate that noise levels in the driver's cabin exhibit consistent patterns across different hours. Nonetheless, the long-term time series analysis revealed periodic characteristics, suggesting regular variations in noise levels throughout the day. These findings underscore the stability and inherent regularity of metro noise signals. Figure 4: The noise signal before and after filtering. (**a**) Noise signal before filtering. (**b**) Noise signal after filtering. Region 1 represents background noise before filtering, region 2 represents squeal before filtering, region 3 represents background noise after filtering, and region 4 represents squeal after filtering. Figure 5: Hourly average sound pressure level (dB). We further calculated the noise evaluation metrics proposed in Section 3.1 for the driver's cab of Lanzhou Urban Rail Transit Line 1 and analyzed its noise environment. The estimation results of noise assessment metrics are shown in Table 3. The acoustic environment within the metro train driver's cabin poses significant health risks to drivers. The equivalent continuous sound level (Leq) of 87.12 dB exceeds the WHO's recommended threshold of 85 dB (as shown in Appendix A Table A2), above which prolonged exposure can result in hearing damage. Furthermore, the maximum recorded noise level (Lmax) of 101.11 dB indicates that drivers are subjected to intermittent peaks that can cause immediate discomfort and contribute to cumulative auditory stress. This noise exposure not only risks hearing loss but can also lead to increased stress levels, fatigue, and impaired concentration, adversely impacting the drivers' performance and safety. The minimum sound pressure level (L\({}_{\text{min}}\)) averages 29.54 dB, with a range from 0.00 dB to 78.78 dB, showing that the noise level can be extremely low during certain periods, possibly when the train is stationary or coasting at low speeds. Percentile noise levels (such as L\({}_{10}\), L\({}_{25}\), L\({}_{50}\), L\({}_{75}\), and L\({}_{90}\)) further reveal the distribution of noise over time.