Welcome to ROSA P | U27 : real-time commercial vehicle safety & security monitoring final report. - 23909 | US Transportation Collection
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.
 
 
Help
Clear All Simple Search
Advanced Search
U27 : real-time commercial vehicle safety & security monitoring final report.
  • Published Date:
    2012-12-01
  • Language:
    English
Filetype[PDF-1.25 MB]


Details:
  • Publication/ Report Number:
    NTRCI-50-2011-026
  • Resource Type:
  • Geographical Coverage:
  • OCLC Number:
    779633741
  • Edition:
    Final report; Dec. 2010-Dec. 2011.
  • NTL Classification:
    NTL-SAFETY AND SECURITY-Speed LimitsNTL-SAFETY AND SECURITY-Highway Safety ; NTL-PLANNING AND POLICY-PLANNING AND POLICY ;
  • Format:
  • Abstract:
    Accurate real-time vehicle tracking has a wide range of applications including fleet management, drug/speed/law enforcement, transportation planning, traffic safety, air quality, electronic tolling, and national security. While many alternative tracking technologies have been developed in the recent years, license plate recognition (LPR) technology is still the simplest and readily available means for uniquely identifying vehicles in more circumstances. LPR technology has gone through quite a bit of research and development since the 1970’s. While the video-imaging based approach was novel, promising, and even effective for license plate identification in the early days of the technology, accuracy of the technology has not improved much largely because of the thousands of different designs of license plates in the U.S. This study takes an entirely different path with LPR. Realizing even when a plate is read incorrectly, certain amount of useful information may still exist in the misread result. For example, when a plate of “ABC 123” is read as, say, “ABC I23,” instead of just admitting defeat, we recognize that: 5/6 of characters were read correctly; the incorrectly read character is very similar to the correct; character, the sequence of the characters are in the right order; the number of characters are correct, etc. By using this information cleverly, one can address the plate-matching and vehicle-tracking problem with much better results. In real-time operational condition, one would not know if a plate reading of “ABC I23” is correct or not and if not which character or characters were incorrect. So a challenging and complex mathematical challenge ensues here. This study boils down to the development of an algorithm for solving this problem and, for this phase particularly, demonstrating the algorithm in the field. Aided by the generous assistance of Tennessee Department of Transportation (TDOT), Tennessee Department of Safety (TDOS), and PIPS Technology, three state-of-the-art LPR machines are installed for capturing real-world license plates strategically located on the Interstate highways. Using 3G cellular data network, license plate information is collected from all LPR sites and processed instantaneous for real-time plate matching, vehicle tracking, and, real-time speed monitoring.

    This study marks the most significant advancement in LPR technology in decades. The results can improve the utility of new and already deployed LPR units and significantly increase the license plate matching rate (from less than 40% to over 98%) without the need for unified license plate system, further LPR hardware enhancement, per-vehicle technology investment (e.g. transponder and RFID), or significant legislative changes.

  • Supporting Files:
    No Additional Files
No Related Documents.
You May Also Like:
Submit Feedback >