Details:

Creators:
Refai, Hazem ; Bitar, Naim ; Schettler, Jesse ; Kalaa, Omar Al
Refai, Hazem ; Bitar, Naim ; Schettler, Jesse ; Kalaa, Omar Al Less -
Corporate Creators:
University of Oklahoma. School of Electrical and Computer Engineering
Corporate Contributors:
United States. Federal Highway Administration
Subject/TRT Terms:
[+]
Accuracy Algorithms Automatic Vehicle Classification Data Collection Errors
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):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:159f569ae47e2bd9de6022f132d85c8fb5e4ed9536925ad771ddba89d1bbf143
Download URL:
https://rosap.ntl.bts.gov/view/dot/29303/dot_29303_DS1.pdf
File Type:

Supporting Files

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