Pavement Performance: Approaches Using Predictive Analytics

Acceptable pavement condition is paramount to road safety. Using predictive analytics techniques, this project attempted to develop models that provide an assessment of pavement condition based on an array of indicators that include pavement distress, pavement type, traffic load, structural data, and pavement repair history. Data collected by the Iowa Department of Transportation (DOT) regarding road conditions across the state of Iowa were used to model pavement condition index (PCI). All data were from calendar year 2013 and consisted of nearly 4,000 observations. Various distress indicators were used to model PCI. These distress measures quantify a variety of cracks (types of cracks, severity of cracks, and amount of cracking) as well as joint spalling (severity and amount) and the condition of previous patching (condition and amount). Twenty-three distress measures were considered as possible model inputs. In addition to distress measures, nine descriptive variables were tested as potential model inputs for improving the overall fit of the model to the data. These descriptive variables included traffic, load, speed limit, number of lanes, pavement thickness, and pavement age. Series of multiple regression models were developed for different pavement types and for combined data (when all pavement types were aggregated). The results reveal that a number of distress variables and descriptive variables have a statistically significant relationship with PCI. The efficacies of the derived models, as measured by R2 values, range from 44% to 86%. The results of further analyses show that the introduction of the quadratic effects of certain variables on PCI improves model efficacy. Therefore, it is concluded that linear predictive models that involve distress and descriptive characteristics of road conditions provide a reasonable basis for estimating PCI. However, these models can be further improved by examining nonlinear effects.

  • Record URL:
  • Supplemental Notes:
    • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    Creighton University

    Heider College of Business, Department of Business Intelligence & Analytics
    2500 California Plaza
    Omaha, NE  United States  68178

    Midwest Transportation Center

    2711 South Loop Drive, Suite 4700
    Ames, IA  United States  50010-8664

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Authors:
    • Duckworth, William
    • Nath, Ravi
  • Publication Date: 2018-3


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Appendices; Figures; References; Tables;
  • Pagination: 30p

Subject/Index Terms

Filing Info

  • Accession Number: 01669702
  • Record Type: Publication
  • Contract Numbers: Part of DTRT13-G-UTC37
  • Created Date: May 9 2018 9:21AM