TY - JOUR
T1 - Forecasting vehicle modes of operation needed as input to 'modal' emissions models
AU - Washington, Simon
AU - Leonard, John D.
AU - Roberts, Craig A.
AU - Young, Troy
AU - Sperling, Daniel
AU - Botha, Jan
PY - 1998
Y1 - 1998
N2 - Current motor vehicle emission modelling efforts in the United States are focusing on forecasting carbon monoxide, hydrocarbon and oxide of nitrogen emissions based on the multivariate distribution of modes of vehicle operations. Examples of variables that describe 'modal' operations include power (acceleration x velocity2) thresholds, acceleration rates cruise speeds, and idie duration. Although these models promise to improve the accuracy of emissions forecasts, we currently lack models to forecast joint distributions of speed and acceleration needed as input. This paper presents interim results of a three-year investigation aimed to develop a statistical model capable of forecasting joint distributions of modal activity. Our initial effort has been to develop an experiment in which modal activity distributions can be collected on freeways and related to various traffic, roadway and contro conditions To streamline a potentially huge full-factorial experiment, we employ the FRESIM simulation model to eliminate second-order effects, leaving only main effects in the experiments. We employ an heuristic hierarchical tree-based regression (HTBR) method to identify important factors. We then parameterize the HTBR model to demonstrate how modes of operation could be forecast given 'real' roadway, traffic, and traffic control conditions.
AB - Current motor vehicle emission modelling efforts in the United States are focusing on forecasting carbon monoxide, hydrocarbon and oxide of nitrogen emissions based on the multivariate distribution of modes of vehicle operations. Examples of variables that describe 'modal' operations include power (acceleration x velocity2) thresholds, acceleration rates cruise speeds, and idie duration. Although these models promise to improve the accuracy of emissions forecasts, we currently lack models to forecast joint distributions of speed and acceleration needed as input. This paper presents interim results of a three-year investigation aimed to develop a statistical model capable of forecasting joint distributions of modal activity. Our initial effort has been to develop an experiment in which modal activity distributions can be collected on freeways and related to various traffic, roadway and contro conditions To streamline a potentially huge full-factorial experiment, we employ the FRESIM simulation model to eliminate second-order effects, leaving only main effects in the experiments. We employ an heuristic hierarchical tree-based regression (HTBR) method to identify important factors. We then parameterize the HTBR model to demonstrate how modes of operation could be forecast given 'real' roadway, traffic, and traffic control conditions.
KW - Classification and regression trees
KW - Mobile source emissions modelling
KW - Travel demand modelling
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U2 - 10.1504/ijvd.1998.001846
DO - 10.1504/ijvd.1998.001846
M3 - Article
AN - SCOPUS:0032287406
SN - 0143-3369
VL - 20
SP - 351
EP - 359
JO - International Journal of Vehicle Design
JF - International Journal of Vehicle Design
IS - 1-4
ER -