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Traffic noise modelling at intersections in mid-sized cities: an artificial neural network approach
Adarsh Yadav1, Manoranjan Parida2, Pushpa Choudhary3
1Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India.
Environmental Monitoring and Assessment
|March 26, 2024
Summary
Traffic noise near intersections significantly impacts urban health. This study developed an artificial neural network (ANN) model using field data to identify key noise influencers like traffic volume and honking for better noise management.
Area of Science:
- Environmental Science
- Urban Planning
- Acoustics
Background:
- Traffic noise is a major environmental concern impacting urban dwellers' health.
- Noise pollution is particularly critical near intersections in mid-sized cities due to inadequate planning and noise mitigation.
- Effective noise assessment models are needed for urban intersection environments.
Purpose of the Study:
- To develop a precise intersection-specific traffic noise model for mid-sized cities.
- To assess traffic noise levels at urban intersections.
- To investigate the influence of various noise-influencing variables on traffic noise.
Main Methods:
- Utilized an artificial neural network (ANN) approach.
- Collected and analyzed 342 hours of field data from nineteen intersections in Kanpur, India.
- Performed sensitivity analysis to determine the impact of different variables.
Main Results:
- Traffic volume, median width, carriageway width, honking, and receiver distance significantly affect traffic noise levels.
- The influence of noise variables varies with proximity to intersections.
- A wider median reduces noise, while noise increases within 50m of the stop line.
Conclusions:
- The developed model provides accurate traffic noise assessment for urban intersections.
- Findings offer insights for creating effective noise management strategies.
- This research supports the development of managerial action plans to mitigate traffic noise pollution in mid-sized cities.
