01624213

Component

The type of pavement management decisions made by transportation agencies are affected by the quality of pavement data. To ensure consistency of data used in pavement management systems, various quality control and acceptance practices are utilized. In this paper, data visualization and conditional inference trees were used to assess the quality of pavement roughness data by identifying potential sources of error in the International Roughness Index (IRI) data from a vendor and the Iowa Department of Transportation (Iowa DOT). A total of 1007 pavement management sections were used in the analysis. The differences between IRI values from the two data sources were expressed as percentage errors. Localization of high percentage errors with respect to county and routes were performed using heat maps. Scatterplots revealed a prevalence of extremely high percentage errors for low DOT IRI values. Conditional inference trees were used to determine how variables contributed to differences between DOT and vendor IRI values. The inference trees separated sections into various groups with different characteristics providing insight on the sources of error in the data. Based on the promising results of this study, conditional inference trees and data visualization approaches highlighted in this paper can serve as robust tools for data quality management.

This paper was sponsored by TRB committee AFD10 Standing Committee on Pavement Management Systems. Alternate title: Quality Assessment of Pavement Roughness Data Using Conditional Inference Trees & Data Visualization Techniques.

01618707

17-02614

English

17p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Data quality; Pavement management systems; Roughness

International Roughness Index

Conditional inference trees; Data visualization

Iowa

Highways; Pavements

Transportation Research Board Annual Meeting 2017 Paper #17-02614

PRP, TRIS, TRB, ATRI

Dec 8 2016 10:59AM