Developing a Model to Estimate PASER using Vertical Acceleration Data

Abstract: Measuring the pavement condition of a transportation network is key to develop a capital improvement plan. Pavement condition of a roadway segment (Pavement Surface Evaluation and Rating (PASER)) indicates when and what type of maintenance is required to fix the segment within a limited budget. This study explored the potential of leveraging smartphone accelerometers, specifically through dedicated applications, to assess Pavement Surface Evaluation and Rating (PASER) of road segments. The research involved a two-pronged data collection approach: vertical acceleration data was captured using smartphone applications, while corresponding PASER ratings were sourced from the Pavement Management System (PMS) database of the City of Fort Wayne. A total of 37 strategically selected representative road segments formed the basis of this data collection endeavor. To establish a quantitative relationship between vertical acceleration and PASER, a statistical model was constructed, treating vertical acceleration as the independent variable and PASER as the dependent variable. The results of this analysis yielded a statistically significant correlation between the two variables, as evidenced by a coefficient of determination of 0.84. This strong correlation suggests that the proposed method could offer a cost-effective and objective alternative to traditional subjective PASER assessments. By automating the rating process and eliminating the potential for inter-rater variability, this approach could enhance the consistency and reliability of pavement condition assessments for local agencies. Additionally, the reduced reliance on manual data collection and analysis could lead to significant cost savings.