TY - JOUR
T1 - Robust modeling and forecasting of diesel particle number emissions rates
AU - Kamarianakis, Yiannis
AU - Oliver Gao, H.
AU - Holmén, Britt A.
AU - Sonntag, Darrell B.
N1 - Funding Information:
We gratefully acknowledge Zhong Chen, Aura Davila and Derek M. Vikara for conducting the field measurements and Eric D. Jackson for assistance with data processing and providing insights for analyzing the data. This research was sponsored by the Joint Highway Research Advisory Council of the University of Connecticut, and the Connecticut Department of Transportation through Projects 03-8 and 05-9.
PY - 2011/8
Y1 - 2011/8
N2 - This paper develops predictive models for high-frequency particle number emissions rates, analogous to the models that have been developed for gaseous emissions and particulate mass. Data from diesel buses under real-world driving conditions is used and predictive models are based on engine operating variables, vehicle kinematic variables, vehicle specific power, and gaseous mass emissions rates. Particular focus is devoted to estimation and forecasting that is robust to outliers and asymmetric error distributions. The models based on a combination of vehicle kinematic variables and gaseous emissions offer good data fits when compared to models based on engine operating variables. Furthermore, least absolute value minimization leads to superior out-of-sample predictive accuracy compared to conventional, least squares minimization.
AB - This paper develops predictive models for high-frequency particle number emissions rates, analogous to the models that have been developed for gaseous emissions and particulate mass. Data from diesel buses under real-world driving conditions is used and predictive models are based on engine operating variables, vehicle kinematic variables, vehicle specific power, and gaseous mass emissions rates. Particular focus is devoted to estimation and forecasting that is robust to outliers and asymmetric error distributions. The models based on a combination of vehicle kinematic variables and gaseous emissions offer good data fits when compared to models based on engine operating variables. Furthermore, least absolute value minimization leads to superior out-of-sample predictive accuracy compared to conventional, least squares minimization.
KW - Buses' gaseous emissions
KW - Emissions modeling and forecasting
KW - Particle number emissions
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U2 - 10.1016/j.trd.2011.04.005
DO - 10.1016/j.trd.2011.04.005
M3 - Article
AN - SCOPUS:79957969879
SN - 1361-9209
VL - 16
SP - 435
EP - 443
JO - Transportation Research Part D: Transport and Environment
JF - Transportation Research Part D: Transport and Environment
IS - 6
ER -