Assessment of Road Dynamics, Environmental Noise Pollution in Urban Corridors

Abstract

Urbanization and the rapid increase in vehicular traffic have led to heightened noise pollution levels in urban areas. This study aims to analyze the relationship between traffic volume and environmental noise pollution at four major urban locations: ABIDS, JUBILEE HILLS, JEEDIMETLA, and JNTU. Key traffic parameters such as traffic volume, Peak Hour Factor (PHF), and Free Flow Speed (FFS) were recorded, while noise measurements including Equivalent Continuous Sound Level (Leq) were collected during both day and night. Additionally, Traffic Noise Index (TNI) and Noise Pollution Level (Lnp) were computed using established methods. A multiple linear regression analysis was conducted to assess the influence of traffic characteristics on noise levels. The high coefficient of determination (R² = 0.905) demonstrates a strong correlation between noise levels and traffic variables like traffic volume, PHF, and roadway capacity. Furthermore, roadway capacity and Level of Service (LOS) were evaluated at each site based on Highway Capacity Manual (HCM) guidelines. LOS values ranged from B to E, reflecting varying degrees of congestion and traffic operational efficiency. The findings highlight the significant role of traffic parameters in contributing to urban noise pollution and road performance. These insights can support urban planners and traffic engineers in designing effective traffic management and noise mitigation strategies, ultimately improving urban environmental quality.

Introduction

Hyderabad faces increasing noise pollution primarily due to:

• Road traffic

• Construction activities

• Excessive honking

• Industrial operations

• Commercial zone activity

Key high-traffic areas such as Abids, Jubilee Hills, JNTU, and Jeedimetla experience consistently high noise levels and congestion. This results in not only environmental degradation but also serious health impacts like hearing loss, high blood pressure, poor sleep, and stress.

Study Objectives:

1. Analyze traffic volumes and determine Peak Hour Factor (PHF), roadway capacity, and Level of Service (LOS).

2. Measure and analyze traffic noise levels at four urban locations.

3. Compute key noise indices: TNI, Lnp, and Ldn.

4. Use Multiple Linear Regression (MLR) to determine how traffic variables influence noise levels (Leq).

Methodology:

• Noise Indices Used:

  • TNI (Traffic Noise Index): Evaluates noise variability.

  • Lnp (Noise Pollution Level): Captures overall noise impact.

  • Ldn (Day-Night Average Sound Level): Accounts for 24-hour exposure.

• Traffic Parameters:

  • PHF, capacity, and LOS based on HCM 2010 standards.

  • Multiple Linear Regression applied to assess traffic's impact on noise.

Key Results:

A. Traffic Analysis:

• PHF Values ranged from 0.88 to 0.947, indicating varying traffic uniformity.

• JNTU showed the most stable flow during evening peak.

B. Noise Level Analysis:

• Permissible CPCB limits:

  • Day: 65 dB (Commercial)

  • Night: 55 dB

• JNTU and Jeedimetla recorded significantly higher noise levels, exceeding both day and night limits.

• ABIDS was the only location to remain within day-time limits.

• TNI values above 74 dB (especially at JNTU) indicate high noise disturbance.

• Ldn values suggest:

  • JNTU (80.48 dB) contributed the highest share (29%) to total noise exposure.

  • Followed by Jeedimetla (25%), Jubilee Hills (24%), and ABIDS (22%).

C. Regression Analysis (Leq as Dependent Variable):

• Regression Equation:

  Leq=−277.367+(0.001166×Traffic Volume)+(246.566×PHF)+(0.045786×Capacity)\text{Leq} = -277.367 + (0.001166 × \text{Traffic Volume}) + (246.566 × \text{PHF}) + (0.045786 × \text{Capacity})Leq=−277.367+(0.001166×Traffic Volume)+(246.566×PHF)+(0.045786×Capacity)

• R² = 0.9055 → 90.55% of noise variation explained by traffic volume, PHF, and capacity.

• Significant Predictors: Traffic volume and PHF.

• Capacity showed limited influence (not statistically significant).

D. Capacity and Level of Service (LOS):

• LOS B (Jubilee Hills): Indicates free flow.

• LOS D (ABIDS & Jeedimetla): Approaching unstable conditions.

• LOS E (JNTU): Reflects congested and poor traffic service.

Conclusion

1) Peak Hour Factor (PHF) values across study sites ranged between 0.88 and 0.947, with JNTU showing the highest uniformity during the evening peak hour. 2) Noise levels at Jeedimetla and JNTU consistently exceeded CPCB permissible limits during both day and night, while ABIDS and Jubilee Hills occasionally crossed the night-time limits. 3) Traffic Noise Index (TNI) indicated severe noise disturbance at JNTU (day and night) and Jeedimetla (daytime), with values above the 74 dB comfort threshold. 4) Lnp and Ldn analyses confirmed that most study locations—except ABIDS (daytime)—experienced noise levels beyond the recommended standards, with JNTU recording the highest exposure. 5) The multiple regression model (R² = 0.9055) showed that traffic volume and PHF are strong predictors of Leq, whereas capacity had minimal influence on noise levels. 6) Capacity and LOS evaluation revealed: • LOS E (severe congestion) at JNTU, • LOS D (unstable flow) at Jeedimetla and Abids. • LOS B (smooth operation) at Jubilee Hills. 7) Overall, locations with higher traffic volumes and poor LOS were found to exhibit elevated noise pollution, highlighting the urgent need for integrated traffic management and noise mitigation strategies in urban areas.

References

[1] Banerjee, D. Chakraborty, S.K. Bhattacharyya, S., & Gangopadhyay, A. (2008). Evaluation and analysis of road traffic noise in Asansol: An industrial town of eastern India. International Journal of Environmental Research and Public Health, 5(3), 165–171. https://doi.org/10.3390/ijerph5030165 [2] Kisku, G. C., Sharma, K., Kidwai, M. M., Barman, S. C., Khan, A. H., Singh, R., & Jain, V. K. (2006). Profile of noise pollution in Lucknow city and its impact on environment. Journal of Environmental Biology, 27(2), 409–412. [3] Zannin, P. H. T., Ferreira, A. M. C., & Szeremetta, B. (2006). Evaluation of noise pollution in urban traffic hubs—Noise maps and measurements. Environmental Impact Assessment Review, 26(3), 301–309. https://doi.org/10.1016/j.eiar.2005.09.004 [4] Margaritis, E., & Kang, J. (2016). Relationship between urban green spaces and other features of urban morphology with traffic noise levels. Science of The Total Environment, 573, 155–170. https://doi.org/10.1016/j.scitotenv.2016.08.039 [5] Pucher, J., & Buehler, R. (2012). City cycling. MIT Press. [6] Singh, N., & Davar, S. C. (2004). Noise pollution—Sources, effects and control. Journal of Human Ecology, 16(3), 181–187. https://doi.org/10.1080/09709274.2004.11905732 [7] Berglund, B., Lindvall, T., & Schwela, D. H. (1999). Guidelines for Community Noise. World Health Organization (WHO). https://apps.who.int/iris/handle/10665/66217 [8] Indian Road Congress (IRC: 106-1990). Guidelines for Capacity of Urban Roads in Plain Areas. New Delhi: IRC Publications. [9] Central Pollution Control Board (CPCB), India. (2000). Ambient Air Quality in Respect of Noise. https://cpcb.nic.in/noise-pollution/ [10] Ministry of Environment, Forest and Climate Change (MoEFCC), India. (2000). The Noise Pollution (Regulation and Control) Rules, 2000. https://moef.gov.in [11] Transportation Research Board. (2010). Highway Capacity Manual (HCM 2010). National Research Council, Washington, D.C. [12] Telangana State Pollution Control Board (TSPCB). (2023). Ambient Noise Monitoring Reports – Hyderabad Region. Retrieved from https://tspcb.cgg.gov.in/

Copyright

Copyright © 2025 Jarpula Kethan Sai, Dr. P. Sravana, Tejavath Naresh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.