Evaluating roadway surface rating technologies.

Details

• Creators:
  Belzowski, Bruce ; Ekstrom, Andrew
• Corporate Creators:
  University of Michigan. Transportation Research Institute
• Subject/TRT Terms:
  • Surface course (Pavements)
  • Roughness
  • Accelerometers
  • Smartphones
  • Software
  • Data collection
  • Feasibility analysis
  • Evaluation
• Publication/ Report Number:
  RC- 1621 ; UMTRI-2015-19
• Resource Type:
  Tech Report
• Geographical Coverage:
  Michigan
• Corporate Publisher:
  Michigan. Dept. of Transportation
• 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.
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• Format:
  PDF
• Funding:
  2013-0068
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29056/dot_29056_DS1.pdf
• File Type:
  [PDF - 1.70 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:ca6574d6272a8b423edf6a5bf761891426a110711187c805d590bfa2a5262e22