Long‐wavelength track irregularities have obvious influence on ride comfort and running stability of high‐speed trains. Meanwhile, it brings risk to the inspection of track irregularities since ordinary inspection equipment has difficulties in covering long wavelengths. Previous research on the effect of long‐wavelength track irregularities is rare. In order to find the relationship between long‐wavelength irregularities and vehicle dynamic responses, a numerical vehicle‐track coupling dynamic model based on multibody dynamics and finite element theories is established by using a self‐compiling program. One case study is given as an example to show the methodology of determining the sensitive long wavelength and management amplitude of track longitudinal‐level irregularities in high‐speed railway. The simulation results show that the sensitive long wavelength has a strong correlation with train speed and natural frequency. The simulation and field test results are in good agreement.

High-speed railway track defects such as rail corrugation, wheel polygonal wear, and rail fastener clip failure are closely related to the modal frequency of the track structure. As the operation time increases, the modal parameters of the track structure will change, and it is necessary to know what they are to diagnose the vibration characteristics. Present methods of modal analysis are limited by measuring points and excitations and cannot be used to extract the operational parameters of the railway track. This paper proposes a novel approach using wheel-rail excitations to identify the rail modal frequency from vibration monitoring data. The approach decomposes the rail acceleration after the wheel-rail excitation passes into intrinsic mode functions and extracts their instantaneous frequencies. The modal frequencies of the rail can be identified from the instantaneous frequencies. To demonstrate the feasibility of the approach, the results using the proposed approach are compared with the modal frequencies of the rail identified using the eigensystem realization algorithm. Then the approach is applied to study the influence of the number of wheel-rail excitations and the number of monitoring points on the identification results in high-speed railway ballastless track rail. The paper concludes that the proposed approach can provide a reliable solution for the identification of the operational modal frequency of track rails based on the monitoring data.