Publication Detail
Estimation of a Vehicular Carbon Monoxide Modal Emissions Model and Assessment of an Intelligent Transportation Technology
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UCD-ITS-RR-94-29 Research Report Alumni Theses and Dissertations |
Suggested Citation:
Washington, Simon P. (1994) Estimation of a Vehicular Carbon Monoxide Modal Emissions Model and Assessment of an Intelligent Transportation Technology. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-94-29
Increased automobile emissions of carbon monoxide (CO) and ozone produced by the mixing of hydrocarbons (HC) and oxides of nitrogen (NOx) in the presence of sunlight have led to the deterioration of air quality in many urban areas. In response, the Federal government has set maximum concentrations of these pollutants that transportation and air quality planners must strive to meet. These national air quality regulations have urged development of mathematical emission prediction models that are precise and accurate.
Evaluations and assessments of the currently employed emission prediction models have shown emission predictions to be underestimated by factors of 2 to 3 in some cases. There are many reasons why current models do not predict automobile emissions under "real world" driving conditions accurately. Among them are: non-representative driving activity used to derive emission prediction algorithms; lack of driving activity input variables; statistical shortcomings of models; and non-representativeness of tested vehicles compared to on-road vehicles.
Given the inaccuracy of current emission prediction models, and given the need to accurately assess transportation control measures, incremental transportation supply changes, and intelligent transportation technologies, there is a great need to develop new emission prediction models. The new models need to be sensitive enough to capture the effects of microscopic flow adjustments, or flow smoothing, that are now commonly considered among transportation and air quality planners.
This dissertation focuses on current technical problems associated with carbon monoxide prediction algorithms contained in emission inventory and impact models. Their most notable problem is the inability to account for emissions contributions from "modal" vehicle activities such as idle, acceleration, deceleration, and high load and power conditions, which have been observed to contribute significantly to emissions.
The main contribution of this research effort is the derivation of an ordinary least squares regression "modal" model for use in estimating carbon monoxide emissions from motor vehicles. The new model, dubbed DITSEM (Davis Institute of Transportation Studies Emission Model), employs "modal" explanatory variables such as acceleration, positive kinetic energy, and idle. Statistical comparisons show that DITSEM is superior to existing carbon monoxide emission prediction algorithms contained in the California Department of Transportation's CALINE4 and the California Air Resources Board's EMFAC7F emission models.
DITSEM is also used to demonstrate the air quality benefits of an Intelligent Transportation System Technology, namely the substitution of electronic toll collection for conventional toll plaza operations. DITSEM emission predictions agree with field observed emissions, and demonstrate its ability to account for modal activities. Competing models fail to predict emissions well under the same circumstances.
Based on the research findings, application of DITSEM within the currently mandated modeling framework is recommended.
Evaluations and assessments of the currently employed emission prediction models have shown emission predictions to be underestimated by factors of 2 to 3 in some cases. There are many reasons why current models do not predict automobile emissions under "real world" driving conditions accurately. Among them are: non-representative driving activity used to derive emission prediction algorithms; lack of driving activity input variables; statistical shortcomings of models; and non-representativeness of tested vehicles compared to on-road vehicles.
Given the inaccuracy of current emission prediction models, and given the need to accurately assess transportation control measures, incremental transportation supply changes, and intelligent transportation technologies, there is a great need to develop new emission prediction models. The new models need to be sensitive enough to capture the effects of microscopic flow adjustments, or flow smoothing, that are now commonly considered among transportation and air quality planners.
This dissertation focuses on current technical problems associated with carbon monoxide prediction algorithms contained in emission inventory and impact models. Their most notable problem is the inability to account for emissions contributions from "modal" vehicle activities such as idle, acceleration, deceleration, and high load and power conditions, which have been observed to contribute significantly to emissions.
The main contribution of this research effort is the derivation of an ordinary least squares regression "modal" model for use in estimating carbon monoxide emissions from motor vehicles. The new model, dubbed DITSEM (Davis Institute of Transportation Studies Emission Model), employs "modal" explanatory variables such as acceleration, positive kinetic energy, and idle. Statistical comparisons show that DITSEM is superior to existing carbon monoxide emission prediction algorithms contained in the California Department of Transportation's CALINE4 and the California Air Resources Board's EMFAC7F emission models.
DITSEM is also used to demonstrate the air quality benefits of an Intelligent Transportation System Technology, namely the substitution of electronic toll collection for conventional toll plaza operations. DITSEM emission predictions agree with field observed emissions, and demonstrate its ability to account for modal activities. Competing models fail to predict emissions well under the same circumstances.
Based on the research findings, application of DITSEM within the currently mandated modeling framework is recommended.
Ph.D. Dissertation