The study of vehicle classification equipment with solutions to improve accuracy in Oklahoma.
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.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

The study of vehicle classification equipment with solutions to improve accuracy in Oklahoma.

Filetype[PDF-2.68 MB]


English
Details You May Also Like

Details:

  • Creators:
  • Corporate Creators:
    University of Oklahoma. School of Electrical and Computer Engineering
  • Corporate Contributors:
    United States. Federal Highway Administration
  • Subject/TRT Terms:
  • Publication/ Report Number:
    FHWA-OK-14-17
  • Resource Type:
    Tech Report
  • Geographical Coverage:
    Oklahoma
  • Corporate Publisher:
    Oklahoma. Dept. of Transportation. Materials and Research Division
  • Abstract:
    The accuracy of vehicle counting and classification data is vital for appropriate future highway and road

    design, including determining pavement characteristics, eliminating traffic jams, and improving safety.

    Organizations relying on vehicle classifiers for data collection should be aware that systems can be

    affected by hardware and sensor malfunction, as well as the equipment’s implementation of classification

    scheme (i.e., algorithm). This report presents outcomes from an extensive statewide examination of

    vehicle misclassification at Oklahoma Department of Transportation (ODOT) AVC stations employing

    the PEEK Traffic ‘FHWA-USA’ classification algorithm. A ground truth system utilizing continuous

    video recordings was developed and utilized. Results from the rigorous investigation are reported herein.

    Also detailed in this report is a novel method for an improved classification algorithm designed to reduce

    the number of classification errors. Thirteen Gaussian distributions were employed to model axle spacing

    for each of the 13 FHWA vehicle types. Classifications obtained from video recordings and PEEK Traffic

    axle spacing measurements for a sample of 20,000 vehicles were recorded and analyzed to obtain 13

    good-fit Gaussian distributions that correspond with each vehicle class. An optimization algorithm was

    then implemented to develop axle spacing thresholds for vehicles currently traveling Oklahoma’s

    highways and to minimize vehicle misclassification. The new scheme was then implemented in the PEEK

    Traffic automatic data record equipment and experimentally evaluated for accuracy. Results demonstrated

    its effectiveness in improving vehicle classifications and reducing persistent overall system errors

    characteristic of the ‘FHWA-USA’ Scheme. Analysis methodology detailed in this report will benefit

    organizations interested in improving vehicle classification and overall system accuracy.

  • Format:
    PDF
  • Funding:
    ODOT SP&R Item Number 2250
  • Collection(s):
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

  • No Additional Files
More +

You May Also Like

Checkout today's featured content at rosap.ntl.bts.gov