Title:
A new heavy-duty vehicle visual classification and activity estimation method for regional mobile source emissions modeling

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dc.contributor.advisor Rodgers, Michael O.
dc.contributor.author Yoon, Seungju en_US
dc.contributor.committeeMember Jennifer H. Ogle
dc.contributor.committeeMember Michael D. Meyer
dc.contributor.committeeMember Michael P. Hunter
dc.contributor.committeeMember Randall L. Guensler
dc.contributor.department Civil and Environmental Engineering en_US
dc.date.accessioned 2005-09-16T15:17:51Z
dc.date.available 2005-09-16T15:17:51Z
dc.date.issued 2005-07-20 en_US
dc.description.abstract For Heavy-duty vehicles (HDVs), the distribution of vehicle miles traveled (VMT) by vehicle type is the most significant parameters for onroad mobile source emissions modeling used in the development of air quality management and regional transportation plans. There are two approaches for the development of the HDV VMT distribution; one approach uses HDV registration data and annual mileage accumulation rates, and another uses HDV VMT counts/observations collected with the FHWA truck classification. For the purpose of emissions modeling, the FHWA truck classes are converted to those used by the MOBILE6.2 emissions rate model by using either the EPA guidance or the National Research Council conversion factors. However, both these approaches have uncertainties in the development of onroad HDV VMT distributions that can lead to large unknowns in the modeled HDV emissions. This dissertation reports a new heavy-duty vehicle visual classification and activity estimation method that minimizes uncertainties in current HDV conversion methods and the vehicle registration based HDV VMT estimation guidance. The HDV visual classification scheme called the X-scheme, which classifies HDV/truck classes by vehicle physical characteristics (the number of axles, gross vehicle weight ratings, tractor-trailer configurations, etc.) converts FHWA truck classes into EPA HDV classes without losing the original resolution of HDV/truck activity and emission characteristics. The new HDV activity estimation method using publicly available HDV activity databases minimizes uncertainties in the vehicle registration based VMT estimation method suggested by EPA. The analysis of emissions impact with the new method indicates that emissions with the EPA HDV VMT estimation guidance are underestimated by 22.9% and 25.0% for oxides of nitrogen and fine particulate matter respectively within the 20-county Atlanta metropolitan area. Because the new heavy-duty vehicle visual classification and activity estimation method has the ability to provide accurate HDV activity and emissions estimates, this method has the potential to significantly influence policymaking processes in regional air quality management and transportation planning. In addition, the ability to estimate link-specific emissions benefits Federal and local agencies in the development of project (microscale), regional (mesoscale), and national (macroscale) level air quality management and transportation plans. en_US
dc.description.degree Ph.D. en_US
dc.format.extent 3365380 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/7245
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject X-Scheme en_US
dc.subject Heavy-duty vehicles
dc.subject VMT estimation
dc.subject Vehicle classification
dc.subject Modal emission model
dc.subject Emissions inventory
dc.subject.lcsh Trucks Classification en_US
dc.subject.lcsh Automobiles Motors Exhaust gas Computer simulation en_US
dc.title A new heavy-duty vehicle visual classification and activity estimation method for regional mobile source emissions modeling en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Rodgers, Michael O.
local.contributor.corporatename School of Civil and Environmental Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 60966a41-3a9f-4f98-ba76-eee075ab75dc
relation.isOrgUnitOfPublication 88639fad-d3ae-4867-9e7a-7c9e6d2ecc7c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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