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A statistical model for estimating oxides of nitrogen emissions from light duty motor vehicles

Elsevier India Pvt Ltd.
Publication Date
DOI: 10.1016/s1361-9209(99)00013-9
  • Exhaust Emission Rate
  • Modal Vehicle Activity
  • Tree Regression
  • Test Cycle
  • Computer Science


Abstract Motor vehicle emission rate models for predicting oxides of nitrogen (NO x) emissions are insensitive to vehicle modes of operation such as cruise, acceleration, deceleration, and idle, because they are based on average trip speed. Research has shown that NO x emissions are sensitive to engine load; hence, load-based variables need to be included in emissions models. Ongoing studies attempting to incorporate these `modal' variables have experienced difficulties with: (1) incomplete and/or non-representative data sets of emissions test data vis-a-vis the modal operating profiles of the tested vehicles; (2) lack of information for predicting on-road operating parameters of vehicles; and (3) non-representative vehicles recruited for emissions tests. The objective of this research was to develop a statistical model for predicting NO x emissions from light-duty gasoline motor vehicles. The primary end use of this model is forecasting, rather than explanation of the factors that affect NO x emissions, which brings to bear different requirements from the statistical model. The three challenges noted above are addressed by: (1) analyzing a data set of more than 13 000 hot-stabilized laboratory treadmill tests on 19 driving cycles (specific speed versus time testing conditions), and 114 variables describing vehicle, engine and test cycle characteristics; (2) making the models compatible with empirical data on how vehicles are being operated in-use; and (3) developing statistical weights to account for the differences in model year distributions between the emissions testing database and the current national on-road fleets. The NO x emissions model is estimated using ordinary least-squares regression techniques, with transformed response variable and regression weights. Tree regression is employed as a tool for mining relationships among variables in the data, with particular focus on identifying useful interactions among discrete variables. Details of the model development process are presented, as well as results for the final model showing the predicted emissions algorithm for the current motor vehicle fleet in Atlanta, GA metropolitan region.

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