Detecting and Classifying Roadway Pavement Anomalies Utilizing Smartphones, Onboard Diagnostic Devices, and Classification Models

Pavements are principal roadway infrastructure assets, and pavement maintenance to the preferred level of serviceability constitutes one of the most challenging problems faced by civil and transportation engineers. The paper discusses the development of a low-cost pavement assessment method and a geographic information system (GIS)-based decision support system (DSS) for the condition assessment of roadway networks. Presented herein is a study on the use of low-cost technology for the data collection, detection and classification of roadway pavement anomalies, by utilizing sensors from smartphones and from automobiles’ on-board diagnostic (OBD-II) devices while vehicles are in movement. The smartphone-based data collection is complimented with robust regression, and various algorithms and classification models for the classification of detected roadway anomalies. The recommended methodology is instantly available, low-cost and accurate, and can be utilized in crowd-sourced applications for roadway assessment and in pavement management systems. Further, the proposed methodology has been field-tested (detection and classification of three types of common roadway anomalies, displaying accuracy levels higher than 90%) and it is currently expanded to cover larger datasets and a bigger number of roadway defect types.

• Supplemental Notes:
  • This paper was sponsored by TRB committee AFD20 Standing Committee on Pavement Condition Evaluation. Alternate title: Detecting and Classifying Roadway Pavement Anomalies Utilizing Smartphones, On-board Diagnostic Devices and Classification Models.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Kyriakou, Charalambos
  • Christodoulou, Symeon E
  • Dimitriou, Loukas
• Conference:
  • Transportation Research Board 96th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2017-1-8 to 2017-1-12
• Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Photos; References; Tables;
• Pagination: 14p
• Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Condition surveys; Data collection; Decision support systems; Defects; Geographic information systems; Pavement management systems; Pavements; Smartphones
• Uncontrolled Terms: Anomaly detection; Classification models; Onboard diagnostics
• Subject Areas: Highways; Pavements;

Filing Info

• Accession Number: 01624794
• Record Type: Publication
• Report/Paper Numbers: 17-04160
• Files: TRIS, TRB, ATRI
• Created Date: Feb 1 2017 1:16PM