## 1 Introduction  
Global socioeconomic advancement has made rail transit systems an infrastructure priority, with the accelerated deployment of rail transit networks and emerging Maglev technologies, in particular. Although high-speed rail systems and Maglev technologies offer unparalleled velocity advantages, their environmental noise emission during operation hasbecome a critical concern in international environmental protection protocols. This dual focus on transportation innovation and acoustic pollution mitigation has made Maglev-induced noise characterization a sustained research priority within the environmental engineering community. Countries have established corresponding railway environmental noise prediction models based on their vehicles and line conditions. Germany proposed a corresponding prediction method for Schall 03 railway noise. This method discretizes the railway line into several small segments; each segment is simplified as a point sound source, and the noise contribution of each point sound source to the reception point is calculated. Finally, the total sound level is obtained using energy superposition. The German prediction model has a wide range of applications, but it involves considerably simplified processing and does not consider the time-domain characteristics of the sound source [1; 2; 3; 4]. In the railway external noise prediction model proposed in Russia, a high-speed train is regarded as a finite long-line sound source, and the equivalent continuous sound pressure level (SPL) at the reference position is calculated using an empirical formula derived from the linear fitting of many test results [5]. The Russian prediction model has poor adaptability to different types of high-speed trains; the prediction method is relatively simple, and the time-domain characteristics of the sound source are not considered [6]. In the construction of Hokuriku Shinkansen in Japan, the high-speed railway noise prediction method (Hokuriku model) was adopted. It divides the noise sources into four categories from bottom to top: structure noise, wheel and rail noise under the vehicle, aerodynamic noise above the vehicle, and collector system noise. In the Hokuriku model, sound sources are classified according to the physical sound mechanism; however, the directivity of sound sources is not considered, and a method for determining the sound power level of each sub-sound source is not provided [7; 8; 9]. The United States of America proposed a railway noise prediction model for high-speed steel-wheel electric locomotives and electric multiple-unit (EMU) trains. In this method, the noise sources of train operation are divided into three categories: traction noise, wheel-rail noise, and aerodynamic noise. The sound exposure level (SEL) of each sound source radiating to the sound point is calculated. The American model is easy to use and has a wide range of applications; however, it does not consider the air absorption and time-domain characteristics of the sound source [10]. The railway environmental noise prediction method adopted in China is based on the \"Technical guidelines for environmental impact assessment-Urban rail transit\" issued by the Ministry of Ecology and Environment, which proposes two prediction models based on different train running speeds [11]. For railways and urban rail transit with speeds lower than 200 km/h, the noise source is regarded as an incoherent finite-length dipole line sound source, and the calculation is divided into two parts: the reference position noise radiation SPL and the noise correction term. For a railway with a speed of 200-350 km/h, the noise sources are divided into three types from bottom to top: lower wheel and rail noise, upper aerodynamic noise, and collector system noise. In this model, the sound power level is considered to be the acoustic parameter, the SPL radiated by each sub-sound source to the reception point is calculated, and the total SPL is obtained via energy superposition [12].