Affiliation:
1. School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China
2. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China
3. Urban Mobility Institute, Tongji University, Shanghai 201804, China
4. Intelligent Transportation System Research Center, Hangzhou City University, Hangzhou 310015, China
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.
Funder
Dual-carbon Research Center of Hangzhou City University
Reference55 articles.
1. Traffic volume and road network structure: Revealing transportation-related factors on PM2.5 concentrations;Yu;Transp. Res. Part D Transp. Environ.,2023
2. Substitutes or complements? Examining effects of urban rail transit on bus ridership using longitudinal city-level data;Yang;Transp. Res. Part A Policy Pract.,2023
3. Mikulski, J. (2013, January 23–26). Concept of On-Board Comfort Vibration Monitoring System for Vehicles. Proceedings of the Activities of Transport Telematics, Katowice, Poland.
4. Analysis of the influence of different means of transport on the level of traffic noise;Figlus;Sci. J. Silesian Univ. Technology. Ser. Transp.,2017
5. Modelling changes in travel behaviour mechanisms through a high-order hidden Markov model;Zhu;Transp. A Transp. Sci.,2022