Abstract

Pavement roughness is an expression of the irregularities in a pavement surface that adversely affect the ride quality of a vehicle. Roughness also affects vehicle delay costs, fuel consumption, tires, and maintenance costs. Roughness is predominantly characterized by the international roughness index (flu), which is often measured with inertial profilers. Inertial profilers are equipped with sensitive accelerometers, a height-measuring laser, and a distance-measuring instrument for measuring vehicle vertical acceleration data and the pavement profile. Modern smartphones are equipped with several sensors including a three-axis accelerometer, which was used in this project to collect vehicle acceleration data with an Android-based application. In the study, acceleration data were double integrated numerically to obtain a pavement profile, which was input into the software program ProVAL. The pavement roughness was then calculated. For the initial validation, pavement profile and acceleration data were collected with both an inertial profiler and the newly developed smartphone application from three test sites. The initial validation results suggest that the newly developed smartphone application can measure IRI with good correspondence to the inertial profiler and with good repeatability between measurement replications. However, calibration is needed for rougher pavement sections because the current analysis techniques do not directly account for acceleration damping resulting from vehicle suspension systems. With improvements in analysis that consider the vehicle suspension effects and additional validation, the approach could be used to reduce the cost of acquiring pavement roughness data for agencies and to reduce user costs for the traveling public by providing more robust feedback about route choice and its effect on estimated vehicle maintenance cost and fuel efficiency.