Improvement of the efficiency of vehicle inspection and maintenance programs through incorporation of vehicle remote sensing data and vehicle characteristics

dc.contributor.advisor Rodgers, Michael O.
dc.contributor.author Samoylov, Alexander V.
dc.contributor.committeeMember Guensler, Randall L.
dc.contributor.committeeMember Hunter, Michael P.
dc.contributor.committeeMember Meyer, Michael D.
dc.contributor.committeeMember Ross, Catherine L.
dc.contributor.department Civil and Environmental Engineering
dc.date.accessioned 2014-01-13T16:53:47Z
dc.date.available 2014-01-13T16:53:47Z
dc.date.created 2013-12
dc.date.issued 2013-11-19
dc.date.submitted December 2013
dc.date.updated 2014-01-13T16:53:47Z
dc.description.abstract Emissions from light-duty passenger vehicles represent a significant portion of total criteria pollutant emissions in the United States. Since the 1970s, emissions testing of these vehicles has been required in many major metropolitan areas, including Atlanta, GA, that were designated to be in non-attainment for one or more of the National Ambient Air Quality Standards. While emissions inspections have successfully reduced emissions by identifying and repairing high emitting vehicles, they have been increasingly inefficient as emissions control systems have become more durable and fewer vehicles are in need of repair. Currently, only about 9% of Atlanta area vehicles fail emissions inspection, but every vehicle is inspected annually. This research addresses explores ways to create a more efficient emissions testing program while continuing to use existing testing infrastructure. To achieve this objective, on road vehicle emissions data were collected as a part of the Continuous Atlanta Fleet Evaluation program sponsored the Georgia Department of Natural Resources. These remote sensing data were combined with in-program vehicle inspection data from the Atlanta Vehicle Inspection and Maintenance (I/M) program to establish the degree to which on road vehicle remote sensing could be used to enhance program efficiency. Based on this analysis, a multi-parameter model was developed to predict the probability of a particular vehicle failing an emissions inspection. The parameters found to influence the probability of failure include: vehicle characteristics, ownership history, vehicle usage, previous emission test results, and remote sensing emissions readings. This model was the foundation for a proposed emissions testing program that would create variable timing for vehicle retesting with high and low failure probability vehicles being more and less frequently, respectively, than the current annual cycle. Implementation of this program is estimated to reduce fleet emissions of 17% for carbon monoxide, 11% for hydrocarbons, and 5% for nitrogen oxides in Atlanta. These reductions would be achieved very cost-effectively at an estimated marginal cost of $149, $7,576 and $2,436 per-ton-per-year for carbon monoxide, hydrocarbons, and nitrogen oxides emissions reductions respectfully.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/50410
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Inspection and maintenance program
dc.subject Emission inspection
dc.subject Remote sensing of vehicle emissions
dc.subject CAFÉ
dc.subject Continuous Atlanta fleet evaluation
dc.subject Carbon monoxide
dc.subject Hydrocarbon
dc.subject Nitrogen oxides
dc.subject Probability of vehicle emission test failure model
dc.subject.lcsh Automobiles Motors Exhaust gas Environmental aspects Georgia Atlanta
dc.subject.lcsh Remote sensing
dc.subject.lcsh Probabilities
dc.title Improvement of the efficiency of vehicle inspection and maintenance programs through incorporation of vehicle remote sensing data and vehicle characteristics
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
thesis.degree.level Doctoral
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