The indirect identification of bridge frequencies through coupled vehicle-bridge dynamics is a critical area of research that underpins the health monitoring of bridges. Traditional methods in this domain, however, impose significant constraints on the parameters and operational velocities of the vehicles involved. These restrictions significantly hamper the real-world applicability of these indirect methods since they cannot be smoothly integrated into the analysis of standard vehicles in normal driving conditions. To bridge this gap in the literature and practice, the current study presents a pioneering approach that capitalizes on the dimensionless response of vehicles in transit to indirectly identify bridge frequencies. The research commences by formulating a set of dimensionless equations characterizing the motion of the vehicle-bridge system. From this theoretical groundwork, the study derives a system state equation and an output signal equation, both predicated upon an enhanced subspace identification technique. This study introduces an innovative equation that captures the dimensionless residual response signal from the dual axles of a single vehicle, incorporating temporal variances in the process. This methodological framework successfully negates the adverse impact of road surface irregularities, effectively sidestepping limitations linked to vehicle parameters within conventional subspace identification methods. The versatility of this approach allows for its application to any typical vehicle in motion across a bridge. Then, the study validates the practicality of the proposed indirect approach for the frequency identification of simply supported beam bridges using the dimensionless response of a dual-axle vehicle. Through rigorous numerical analyses, this study examines the influence of driving speeds, road surface conditions, and stochastic vehicle loads on the indirect identification of bridge frequencies. The results highlight the necessity of adequate load excitation to dependably identify bridge frequencies, especially for eliciting the higher-order modal vibrations of bridges, which are essential for accurately identifying modes at higher frequencies. Finally, empirical evidence is provided through field tests conducted on a high-pier simply supported beam bridge. By inputting the monitored dynamic contact force between the vehicle and bridge into the proposed enhanced subspace identification model, this study validates the feasibility and accuracy of this novel approach. The experimental results affirm that the short-time stochastic subspace identification(ST-SSI) technique effectively isolates the first two modal frequencies of the bridge, outperforming the multivariable output error state space(MOESP) method in identifying higher-frequency modes. This research substantially broadens the scope of bridge frequency identification to include standard vehicles within regular traffic flows, simultaneously improving the precision of frequency detection, especially for higher-order modes.