Welcome to ROSA P |
Stacks Logo
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.
Clear All Simple Search
Advanced Search
Evaluating roadway surface rating technologies.
  • Published Date:
  • Language:
Filetype[PDF-1.70 MB]

  • Resource Type:
  • Geographical Coverage:
  • Abstract:
    The key project objective was to assess and evaluate the feasibility and accuracy of custom software used in smartphones to measure road roughness from the accelerometer data collected from smartphones and compare results with PASER (Pavement Surface and Evaluation Rating System) and IRI (International Roughness Index) measurement values collected from the same roadway segments. This project is MDOT’s first large implementation of a customized Android smartphone to collect road roughness data using a methodology developed from previous research work performed by UMTRI. Accelerometer data collection was performed via Android-based smartphones using a customized software application called DataProbe. During the project’s initial phase smartphones were installed in each of nine Michigan Department of Transportation (MDOT) vehicles driven by MDOT employees. These same vehicles also were used during 2012 and 2013 to collect data on road distress using PASER Ratings for comparison. The DataProbe software application was used to collect data and transmit it to a University of Michigan Transportation Research server, where it was sorted, stored, and analyzed. All MDOT regions are represented in this analysis that compares road roughness ratings for nearly 6000 one tenth of a mile road segments. For the second phase of the project, road distress (PASER Rating) data was collected in 2014 simultaneously with an MDOT vehicle equipped with an IRI device and two DataProbe smartphones and two UMTRI vehicles equipped with five DataProbe smartphones. The analysis of the 2012 and 2013 data found that there were a number of significant predictors of IRI road roughness including: the phone and the vehicle used to collect the data, the speed of the vehicle collecting the data, the type of road surface, date of data collection, and accelerometer variance. By including quadratic terms to adjust for non-linear relationships and interactions among the predictors studied in this project, the multiple regression model predicted nearly 45 percent and 43 percent of the variance in IRI values, respectively. An analysis of commonly used IRI categories (3 level/5 level) using ordinal logistic regression found that DataProbe accurately predicted these categories 68/71 percent of the time (2012 data), 77/76 percent of the time (2013 data). Analysis of the data collected in 2014 showed multiple regression models with variance among accelerometer measurements and speed accounting for 37 percent of the variance, while the ordinal logistic regression accurately predicted the IRI (3 level/5 level) categories 86/83 percent of the time. These results are promising when considering the near term application of the DataProbe technology for smaller locales that drive over their local roads more often, generating web-based road roughness visuals of each of the roads in their jurisdiction. In the longer term, statewide road roughness measurement may be performed through the crowd-sourcing model available through Connected Vehicle initiatives, where all vehicles will be equipped with devices that support safety applications as well as other applications such as those that measure road roughness.
  • Format:
  • Funding:
  • Main Document Checksum:
  • Supporting Files:
    No Additional Files
No Related Documents.