Binary Recursive Partitioning Method for Modeling Hot-Stabilized Emissions From Motor Vehicles

Abstract

An alternative statistical modeling approach, hierarchical tree-based regression (HTBR), is presented for developing modal correction factors for hydrocarbon (HC) emissions from motor vehicles. The term modal refers to operating modes of vehicle activity such as cruise, idle, deceleration, and acceleration. Explanation of the statistical theory is provided, followed by a presentation of specific modeling results for HCs. The modeling results are based on 4,800 vehicle emissions tests representing 29 laboratory testing cycles. HTBR methods are indicated to overcome statistical difficulties that are problematic for classical ordinary least-squares (OLS) regression, a commonly applied statistical technique for analyzing emissions data. HTBR methods are more adept at treating interactions and monotonic transformations on independent variables, better at handling categorical independent variables with more than two levels, not adversely affected by multicollinearity, and good at capturing nonadditive behavior across the range of independent variables. Unfortunately, HTBR theory is less well developed than OLS regression theory, and statistical parameter properties, such as efficiency, unbiasedness, and consistency, need further development. The HTBR modeling results for HCs are insightful. Hydrocarbon emissions from normal-emitting motor vehicles are most sensitive to changes in power (instantaneous speed² ƃ acceleration) requirements of a given driving sequence, while high-emitting vehicles are sensitive to both the amount of idle activity and positive kinetic energy (instantaneous speed ƃ acceleration) in a given driving sequence. Vehicle model year, engine size (cubic centimeters of displacement), curbside weight, and fuel delivery type (fuel injected, throttle body injected, carbureted), also were indicated to influence emission rates. Finally, high- and normal-emitting vehicles are sensitive to different operational and vehicle specific factors.

Get full access to this article

View all access options for this article.

References

NCHRP Report, Project 25-7: Improving Transportation Data for Mobile Source Emissions Estimates. TRB, National Research Council, Washington, D.C., July 1996.

Washington

. Estimation of a Motor Vehicle Emissions Model and Assessment of an Intelligent Transportation Technology. Ph.D. dissertation. Institute of Transportation Studies, University of California at Davis, 1994.

Washington

. A Cursory Analysis of EMFAC7G: Reconciling Observed and Predicted Emissions. Research Report prepared for the Union of Concerned Scientists. Institute of Transportation Studies, University of California at Davis, May 1994.

Croes

B. E.

, and Fujita

E. M.

. California’s Motor Vehicle Emission Inventory: Top Down and Bottom Up Assessments of Accuracy. California Air Resources Board, Sacramento, 1993.

Guensler

. Vehicle Emission Rates and Average Vehicle Operating Speeds. Ph.D. dissertation. Institute of Transportation Studies, University of California at Davis, 1993.

Guensler

. Data Needs for Evolving Motor Vehicle Emission Modeling Approaches. In Transportation Planning and Air Quality II ( Benson

Paul

, ed.), American Society of Civil Engineers, New York, 1993.

Purvis

Sensitivity of Transportation Model Results to Uncertainty in Input Data; Transportation Modeling Tips and Trip-Ups. Proc., Air and Waste Management Association Specialty Conference, Pittsburgh, Pa., March 1992.

Transportation Research Circular 389: Environmental Research Needs in Transportation. TRB, National Research Council, Washington, D.C., 1992.

Pollack

, Heiken

, and Gorse

R. A.

. Comparison of Remote Sensing Data and Vehicle Emissions Models: The High Proportion of Emissions from High Emitting Vehicles. Proc., 85th Annual Meeting of the Air and Waste Management Association, Pittsburgh, Pa., June 1992.

10.

Cicero-Fernandez

, and Long

J. R.

. Modal Acceleration Testing on Current Technology Vehicles. Proc., Emission Inventory: Perception and Reality, Pasadena, Calif., Oct. 1993.

11.

Code of Federal Regulations, Title 40, Part 86, Appendix I, July 1, 1990.

12.

User Guide to MOBILE5. EPA-AA-AQAB-94-91. Air Quality Analysis Branch, EPA, Ann Arbor, Mich., 1994.

13.

Breiman

, Friedman

, Olshen

, and Stone

. Classification and Regression Trees. Wadsworth International Group, Belmont, Calif., 1984.

14.

S-Plus Guide to Statistical and Mathematical Analysis: Version 3.3 for Windows. StatSci Division, MathSoft, Inc., Seattle, Wash., 1995.

15.

Neter

, Kutner

, Nachtsheim

, and Wasserman

. Applied Linear Statistical Models, 4th ed. Irwin, Chicago, Ill., 1996.

16.

Prager

CART/CMAC Hybrid: Regression Trees with Interpolation. Proc., International Conference on Pattern Recognition 2, Piscataway, N.J., IEEE, 1994.

17.

Waterhouse

S. R.

, and Robinson

. Pruning and Growing Hierarchical Mixtures of Experts. Proc., Artificial Neural Networks, Cambridge, U.K., IEE, 1995.

18.

Grajski

, Breiman

, Di Prisco

, and Freeman

. Classification of EEG Spatial Patterns With a Tree-Structured Methodology: CART. IEEE Transactions on Biomedical Engineering, Vol. BME-33, No. 12, Dec. 1986.