Noise Data Quantitative Analysis for Heavy-Traffic Roads in East and South Jakarta

¹ Information Systems, Sampoerna University, South Jakarta, Greater Jakarta 12780, Indonesia
² Computer Science, Respati Indonesia University, East Jakarta, Greater Jakarta 13890, Indonesia
³ Information Technology, Darma Persada University, East Jakarta, Greater Jakarta 13450, Indonesia

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Urban traffic noise pollution is a growing environmental concern affecting public health, urban development, and life quality. This research aimed to understand current situation of noise in the normal traffic, collected and processed data of noise levels from various high traffic areas in Jakarta. This research used primary, quantitative, and statistical analysis to assess noise exposure in high-traffic areas and found significant variations in LAeq, Max Level, and LC Peak measurements. Findings showed that excessive noise levels were beyond safe exposure limits, even reaching a peak value of 124.4 dB. Prolonged exposure to noise that exceeded safe exposure levels may lead to stress, sleep problems, and heart-related conditions. The weak correlation (R² = 0.0419) of noise levels with time found in this study suggests that external factors like traffic volume and city infrastructure are more important than time of day in determining noise levels in the city. Acknowledging past research on the negative implications of noise pollution on population’ wellbeing, this study’s findings underscore the need for better noise mitigation policies. These include improved traffic control, increased adoption of low-noise vehicles, tighter restrictions on vehicle noise emissions, and overall, a more effective urban traffic planning and implementation. In addition to traffic control measures, greater use of sound-absorbing construction materials for urban infrastructure and intensified public awareness campaigns on the dangers of noise pollution are essential. Future research should use advanced predictive models that integrate a wider range of environmental variables.