Abstract: Sparse regularization has been proven to be effective in addressing the ill-posed problem in moving force identification （MFI）. However， existing methods often neglect frequency characteristic disparities between static and dynamic components in moving loads， thereby limiting the identification accuracy. Therefore， an MFI method integrating response prior information and weighted dictionary is proposed. A linear relationship between vehicle-induced bridge responses and moving vehicle loads is estab? lished in bridge-vehicle system. Once frequency domain analysis is separately performed on bending moment and acceleration re? sponses， the obtained frequency prior information is then employed to construct weighted dictionaries that correspond to both static and dynamic load components. Subsequently， the static and dynamic components of moving loads are individually solved by alter? nating direction method of multipliers （ADMM）. The effectiveness of proposed method is demonstrated through numerical simula? tions on a real bridge， and a series of MFI experiments are conducted in laboratory. Results show that the weighted dictionaries con? sidering response prior information significantly improves the accuracy of force identification and enhance its robustness to noise.