Abstract
The importance of bicycle transportation is rapidly increasing. However, to evaluate long-distance bicycle lanes, existing pavement surface monitoring techniques are not applicable, quantitative, or efficient. In this paper, a simple and accurate road profile estimation system was developed using a bicycle and a smartphone. A Kalman filter algorithm integrates multiple sensor data to estimate the profile. The accuracy of the proposed algorithm was experimentally evaluated on test courses. As a result, the coefficient of the determination of the estimated profile with the measured profile by a road profiler was above 0.81 and IRI error was below 14%. The estimated profile was highly accurate in the wide spatial frequency range; the proposed algorithm can thus accurately evaluate pavement condition of a variety of scale.
