Airborne LiDAR: A New Source of Traffic Flow Data

Grejner-Brzezinska, Dorota A.; Toth, Charles; McCord, Mark; Ohio State University. Center for Mapping

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Airborne LiDAR: A New Source of Traffic Flow Data

2005-10-01
By Grejner-Brzezinska, Dorota A. ; Toth, Charles ; McCord, Mark

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English

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Creators:

Grejner-Brzezinska, Dorota A. ; Toth, Charles ; McCord, Mark

Grejner-Brzezinska, Dorota A. ; Toth, Charles ; McCord, Mark Less -
Corporate Creators:

Ohio State University. Center for Mapping
Corporate Contributors:

Ohio. Office of Aerial Engineering ; Ohio. Dept. of Transportation ; United States. Federal Highway Administration

Ohio. Office of Aerial Engineering ; Ohio. Dept. of Transportation ; United States. Federal Highway Administration Less -
Subject/TRT Terms:

[+]

Motor Vehicles--Ohio--Remote Sensing Optical Radar Optical Radar--Ohio Remote Sensing Traffic Flow Traffic Flow--Ohio--Remote Sensing
Publication/ Report Number:

FHWA/OH-2005/14
Resource Type:

Tech Report
Geographical Coverage:

Ohio
OCLC Number:

665083675
Corporate Publisher:

Ohio State University. Center for Mapping
NTL Classification:

NTL-OPERATIONS AND TRAFFIC CONTROLS-Traffic Flow
Abstract:

LiDAR (or airborne laser scanning) systems became a dominant player in high-precision spatial data acquisition to efficiently create DEM/DSM in the late '90s. With increasing point density, new systems are now able to support object extraction, such as extracting building and roads, from LiDAR data. The novel concept of this project was to use LiDAR data for traffic flow estimates. In a sense, extracting vehicles over transportation corridors represents the next step in complexity by adding the temporal component to the LiDAR data feature extraction process. The facts are that vehicles are moving at highway speeds and the scanning acquisition mode of the LiDAR certainly poses a serious challenge for the data extraction process. The OSU developed method and it implementation, the I_FLOW program, have demonstrated that LiDAR data contain valuable information to support vehicle extraction, including vehicle grouping and localizations. The classification performance showed strong evidence that the major vehicle categories can be efficiently separated. The I_FLOW program is ready for deployment.
Format:

PDF
Funding:

134145
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:67dfc0e50b36b9bb9d3d14be1a1c8b4ba632ecc3ddbf365fdfb51877cf37ac6c
Download URL:

https://rosap.ntl.bts.gov/view/dot/5726/dot_5726_DS1.pdf
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Airborne LiDAR: A New Source of Traffic Flow Data

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