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The pavement of a road that is subject to deterioration due to vehicle loads needs quantitative and frequent evaluation. The road profile, which determines the ride quality, is an important property, though its estimation is usually costly and thus, infrequent. In this study, a road profile estimation method using an ordinary-vehicle's responses measured by only a smartphone is developed. The algorithm consists of two steps. At first, an ordinary vehicle is modelled as a half car (HC) and a genetic algorithm (GA) identifies its parameters by using the responses of the vehicle passing over a known-size hump. With the estimated vehicle model, an augmented Kalman filter, in which the road profile is included in the state vector, estimates the road profile; Rauch-Tung-Streiber (RTS) smoothing is employed to improve the accuracy. The observation variables and locations are determined based on an observability analysis. A numerical simulation is conducted to investigate the profile estimation performance in terms of drive speeds, model error, and measurement noise. The experiment is carried out on a 13 km road. The profiles estimated by three types of ordinary vehicles are compared with a reference profile obtained by a laser profiler to validate the proposed method. Results from both the simulation and the experiment show that the combination of the vehicle parameter estimation and the profile estimation methods accurately estimates the road profile with a high degree of accuracy and robustness. • Response-based road profile estimation using a smartphone on a vehicle is proposed. • A pitching HC model with 10 unknowns is calibrated when it passes over a hump. • The difference in dynamic properties, sensor locations, and speeds are compensated. • The accuracy of estimation is confirmed using a high-accuracy laser profiler. [ABSTRACT FROM AUTHOR]