Road Pavement Condition Monitoring by Embedded Crowdsensing

This project investigates the design, implementation, and testing of a crowdsensing-based system that allows for pavement condition monitoring in a low-cost, reliable, and rapid manner. It also studies the incentive mechanisms for pavement crowdsensing that properly stimulate users to complete sensing tasks within the budget of platform cost and overall completion time. The authors have tested the pavement crowdsensing system with the goal to ensure that it conforms to functional and nonfunctional requirements. The authors have designed a platform-driven greedy algorithm with nine incentive mechanisms and evaluated their performance; these methods can avoid the cost explosion problem observed in data-reverse-auction incentive mechanisms, and the best of them can reduce the overall completion time by half compared to task-reverse-auction incentive mechanisms. Additional simulations and experiments are recommended to be carried out in the future to study the performance of the pavement crowdsensing system and associated incentive mechanisms in large-scale deployment.

• Record URL:
  https://r3utc.psu.edu/files/2021/06/CIAMTIS-LU-CIAM-COR-R7_s.pdf
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/56366
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Lehigh University

  Bethlehem, PA  United States  18015

  Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS)

  Pennsylvania State University
  University Park, PA  United States  16802

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Cheng, Liang
  • 0000-0002-1615-9169
• Publication Date: 2021-2-27

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Appendices; Figures; References;
• Pagination: 31p

Subject/Index Terms

• TRT Terms: Algorithms; Crowdsourcing; Data collection; Incentives; Mobile applications; Monitoring; Pavement distress
• Subject Areas: Data and Information Technology; Highways; Pavements;

Filing Info

• Accession Number: 01776516
• Record Type: Publication
• Report/Paper Numbers: CIAM-COR-R7
• Contract Numbers: 69A3551847103
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Jul 14 2021 1:43PM