Crowdsensing-Based Consensus Incident Report for Road Traffic Acquisition

Real-time road traffic information brings great convenience for drivers. Various road information acquisitions are enabled by recent mobile crowdsensing paradigm. However, the accuracy of information can not be guaranteed, and appropriate incentive mechanism is still unavailable. In this paper, the authors study the problem of extracting the actual road traffic information according to the reports from an amount of unknown contributors. To obtain the accurate road traffic result with high probability, they establish a reputation system to evaluate the reliability of each contributor, which takes both location and time deviation factors into account. They also design an incentive mechanism to elicit the truthful report of each qualified contributor. Furthermore, the authors improve the existing answer inference methods and derive the correct result in an efficient way. Extensive simulations are carried out to evaluate the proposed algorithms.

• Record URL:
  • https://doi.org/10.1109/TITS.2017.2750169
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Wang, Xiong
  • Zhang, Jinbei
  • Tian, Xiaohua
  • Gan, Xiaoying
  • Guan, Yunfeng
  • Wang, Xinbing
• Publication Date: 2018-8

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 2536-2547
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 19
  • Issue Number: 8
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Algorithms; Crowdsourcing; Data collection; Incident detection; Incident management; Smartphones; Traffic incidents
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01679876
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Aug 31 2018 10:19AM