A Prediction Method for City Traffic Noise Based on Traffic Simulation under a Mixed Distribution Probability

Haibo Wang, Zhaolang Wu and Jincai Chen ()
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Haibo Wang: School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Zhaolang Wu: School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Jincai Chen: School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China

Sustainability, 2024, vol. 16, issue 16, 1-16

Abstract: Predicting and assessing urban traffic noise is crucial for environmental management. This paper establishes a traffic noise simulation method based on microscopic traffic simulation, utilizing a traffic simulation under a mixed distribution probability combining normal and exponential distributions. This method integrates a single-vehicle noise prediction model to compute the spatial distribution of noise. Comparison with empirical data demonstrates that the proposed model effectively predicts the level of traffic noise. The accuracy of the model is validated through comparison with measured data, showing minimum and maximum errors of 3.60 dB(A) and 4.37 dB(A), respectively. Additionally, the noise spatial results under microscopic traffic models are compared with those under line source models, revealing that the proposed model provides a more detailed and realistic noise spatial distribution. Furthermore, the noise variation patterns between stable and time-varying traffic flows are investigated. Results indicate that noise levels fluctuate under stable traffic flow, whereas under time-varying traffic flow, noise values exhibit a stepped change.

Keywords: traffic noise; traffic simulation; mixture distribution; noise spatial distribution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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