Details:

Creators:
Coifman, Benjamin ; Lee, Ho ; Kim, Seoungbum
Coifman, Benjamin ; Lee, Ho ; Kim, Seoungbum Less -
Corporate Creators:
Ohio State University
Subject/TRT Terms:
[+]
Detection And Identification System Applications Highway Traffic Monitoring LIDAR Performance Evaluation Portable Equipment Portable Nonintrusive Vehicle Detection Radar Traffic Surveillance Vehicle Classification
Publication/ Report Number:
FHWA/OH-2012/7
Resource Type:
Tech Report
Geographical Coverage:
Ohio
Edition:
Executive summary report.
Corporate Publisher:
Ohio. Dept. of Transportation
Abstract:
Vehicle classification data are used in many transportation applications, including: pavement design,

environmental impact studies, traffic control, and traffic safety. Typical of most developed countries, every

state in the US maintains a network of vehicle classification stations to explicitly sort vehicles into several

classes based on observable features, e.g., length, number of axles, axle spacing, etc. Various

technologies are used for this automated classification, the three most common approaches are: weigh in

motion (WIM); axle-based classification from a combination of loop detectors, piezoelectric sensors or

pneumatic sensors; and length-based classification from dual loop detectors. Each sensor technology has

its own strengths and weaknesses regarding costs, accuracy, performance, and ease of use.

As noted in the Traffic Monitoring Guide [1], the quality of data collected depends on the operating

agency to periodically calibrate, test, and validate the performance of classification sensors. However,

such a periodic performance monitoring has been prohibitively labor intensive because the only option

has been to manually validate the performance, e.g., classifying a sample by hand. Furthermore, the

manual classifications are prone to human error and conventional aggregation periods allow classification

errors to cancel one another.

To address these challenges, this study examined three interrelated facets of vehicle classification

and classification performance monitoring. First, we manually evaluate the performance of vehicle

classification station on a per-vehicle basis, second we develop a portable LIDAR (light detection and

ranging) based vehicle classification system that can be rapidly deployed, and third we use the LIDAR

based system to automate the manual validation done in the first part using the tools from the second

part.


Format:
PDF
Funding:
State job no. 134516
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:921dcc1859b0697be31d46e5010cef547e05da0c62b301fbfa19726fbe4ac8a0
Download URL:
https://rosap.ntl.bts.gov/view/dot/24434/dot_24434_DS1.pdf
File Type:

Supporting Files

• No Additional Files

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