Publication Detail
Vehicle Emission Rates and Average Vehicle Operating Speeds
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UCD-ITS-RR-93-24 Research Report |
Suggested Citation:
Guensler, Randall L. (1993) Vehicle Emission Rates and Average Vehicle Operating Speeds. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-93-24
This dissertation first describes the mobile source emission modeling regime and establishes the major sources of emission rate uncertainty. A single aspect of the modeling regime, speed correction factor algorithms, which are used to assess motor vehicle emissions at various average operating speeds, is pursued throughout the remainder of the manuscript. The research findings demonstrate that the data and analytical methods employed in regulatory agency analyses to derive speed correction factors result in estimates with high standard errors. The statistical shortcomings of the existing modeling approach include: 'data screening' techniques, data aggregation techniques, and model functional form.
To analyze speed-emission relationships, the existing modeling approaches of the US Environmental Protection Agency and California Air Resources Board are first outlined. The next few chapters work within the constraints of California's existing speed correction factor (SCF) models, where average speed of emission testing cycle is the single independent variable, and four vehicle technology groups are used. A new weighted-disaggregate speed correction factor modeling approach is developed which results in derived SCFs that better predict emission rates when compared to the existing aggregate speed correction factor modeling approach. The most important component of the research presented in the first section of the dissertation is the development of confidence and prediction intervals associated with using the speed-related outputs from emission models.
As evidenced by analysis of the weighted residuals for the new model, however, problems still exist within the revised modeling approach. Model residuals are not normally distributed and heteroscedasticity is noted. Hence, the confidence and prediction intervals derived in the first part of the dissertation, based upon assumed normality, serve only as approximations that require further refinement. The non-normal residuals spurred further investigation into the parent distribution of error terms. The true error bounds associated with emission change estimates from the speed algorithms were estimated through "bootstrap" Monte Carlo analysis. The bootstrap methodology is outlined in the dissertation and the analytical results that better quantify carbon monoxide speed correction factor confidence intervals for recent model year vehicles are then presented.
To analyze speed-emission relationships, the existing modeling approaches of the US Environmental Protection Agency and California Air Resources Board are first outlined. The next few chapters work within the constraints of California's existing speed correction factor (SCF) models, where average speed of emission testing cycle is the single independent variable, and four vehicle technology groups are used. A new weighted-disaggregate speed correction factor modeling approach is developed which results in derived SCFs that better predict emission rates when compared to the existing aggregate speed correction factor modeling approach. The most important component of the research presented in the first section of the dissertation is the development of confidence and prediction intervals associated with using the speed-related outputs from emission models.
As evidenced by analysis of the weighted residuals for the new model, however, problems still exist within the revised modeling approach. Model residuals are not normally distributed and heteroscedasticity is noted. Hence, the confidence and prediction intervals derived in the first part of the dissertation, based upon assumed normality, serve only as approximations that require further refinement. The non-normal residuals spurred further investigation into the parent distribution of error terms. The true error bounds associated with emission change estimates from the speed algorithms were estimated through "bootstrap" Monte Carlo analysis. The bootstrap methodology is outlined in the dissertation and the analytical results that better quantify carbon monoxide speed correction factor confidence intervals for recent model year vehicles are then presented.
Ph.D. Dissertation.