Development of in-vehicle noise prediction models for Mumbai Metropolitan Region, India

Journal of Traffic and Transportation Engineering (English ed. Online)

Nagendra R. Velaga¹  Vishal Konbattulwar²  R.B. Sharmila²  Saurav Jain² 

[1] Civil Engineering Department, Indian Institute of Technology Bombay, Mumbai 400076, India;Transportation Systems Engineering, Civil Engineering Department, Indian Institute of Technology Bombay, Mumbai 400076, India;

关键词: Traffic noise;    Traffic volume;    Speed;    Decibel;    Mumbai;   

DOI  :  10.1016/j.jtte.2016.04.002

来源: DOAJ

【 摘 要 】

Traffic noise is one of the major sources of noise pollution in metropolitan regions causing various health hazards (e.g., long-term sleep disturbance, increase in blood pressure, physical tension, etc.). In this research, noise prediction models, which can measure the noise level experienced by the commuters while driving or traveling by motorized vehicles in the Mumbai Metropolitan Region, India, were developed. These models were developed by conducting a comprehensive study of various factors (e.g., vehicle speed, traffic volume and road characteristics, etc.) affecting the levels of concentration of noise. A widespread data collection was done by conducting road trips of total length of 403.80 km via different modes of transport, such as air-conditioned (A/C) car, non A/C car, bus and intermediate public transport (i.e., traditional 3-wheeler autos). Multiple regression analyses were performed to develop a functional relation between equivalent noise levels experienced by passengers while traveling (which was considered as a dependent variable) and explanatory variables such as traffic characteristics, vehicle class, vehicle speed, various other location characteristics, etc. Noise levels are generally higher in the vicinity of intersections and signalized junctions. Independent data sets (for each mode of transport) were used to validate the developed models. It was noted that maximum differences between observed and estimated values from the model were within the range of ±7.8% of the observed value.

【 授权许可】

Unknown