Predicting pavement condition index using international roughness index in Washington DC.
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Predicting pavement condition index using international roughness index in Washington DC.

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English
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  • Creators:
  • Corporate Creators:
    Howard University. Transportation Research Center
  • Corporate Contributors:
    United States. Federal Highway Administration
  • Subject/TRT Terms:
  • Publication/ Report Number:
    DDOT-RDT-14-03
  • Resource Type:
    Tech Report
  • Geographical Coverage:
    Washington (District of Columbia)
  • Corporate Publisher:
    District of Columbia. Dept. of Transportation
  • Abstract:
    A number of pavement condition indices are used to conduct pavement management assessments, two of which are the

    International Roughness Index (IRI) and Pavement Condition Index (PCI). The IRI is typically measured using specialized

    equipment that calculates the smoothness and ride quality of the roadway segment based on established computer

    algorithms, while the PCI is based on subjective rating of the number of pavement distress. The literature suggests that

    most pavement indices are related, as a result of which several jurisdictions have developed models to predict one index

    from the other(s). This study used three (3) years of IRI-PCI data obtained from the District Department of Transportation

    to develop models which could potentially predict PCI from IRI by functional classification and by pavement type. The

    regression models explored were developed using the ordinary least squares method and were tested on the basis of 5%

    level of significance. The IRI-PCI models yielded R2

    and adjusted R2

    values between 0.008 and 0.0730, indicating that the

    models could only explain up to 7.3% of the variations in the data. In addition, the root mean square errors of the models

    were all determined to be greater than 1. Even though the results of the ANOVA tests indicated that the coefficients

    were generally statistically significant, the low R2

    values and high RMSEs indicate that the models do not adequately

    predict PCI from IRI, within the margin of error. A more sophisticated prediction tool, such as artificial neural networks,

    could be explored to potentially predict PCI from IRI more accurately.

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