Electric vehicles, known for their low-noise emission, are popular and widespread in metropolises in China, and they provide an opportunity for a reduction in environmental noise from vehicles. To understand the noise from electric vehicles better, this study develops noise emission models considering speed, acceleration, and motion state. The model construction is based on the data collected from a pass-by noise measurement experiment in Guangzhou, China. The models describe a linear relationship between the noise level, the logarithm of speed, and the acceleration for multiple motion states (i.e., the constant-speed state, the acceleration state, and the deceleration state). From the spectrum analysis, the low-frequency noise is barely affected by the speed and acceleration, but the noise at a certain frequency is most sensitive to them. Compared to other models, the proposed ones have the highest accuracy and the greatest ability for extrapolation and generalization.

Seeking a straightforward and efficient method to predict expressway traffic noise, this study selected three expressway segments in Guangdong Province, China and conducted noise monitoring at ten different sites along these expressways. Data analysis revealed that the mean sound levels and standard deviations were significantly positively and negatively correlated with traffic volume, respectively, and the frequency distribution of sound levels closely resembled a normal distribution. A probability prediction model for expressway traffic noise, based on a normal distribution, has been constructed utilizing these characteristics. The mean and standard deviation of the model were determined using a linear regression method, and the relationship between the mean, standard deviation, and various noise evaluation indices was derived from the characteristics of the normal distribution. The proposed model enables the direct prediction of the statistical frequency distribution of sound levels and various noise evaluation indices. Despite using only two five-minute segments of monitoring data for training, the model’s average prediction error for L_eq, L₁₀, L₅₀, and L₉₀ was only 1.06, 1.07, 1.04, and 1.32 dB(A). With increased sample data for modeling, the model’s predictive accuracy notably improved. This study provides a highly effective predictive tool for assessing traffic noise for residents near expressways.