Robust modeling and forecasting of diesel particle number emissions rates

Yiannis Kamarianakis, H. Oliver Gao, Britt A. Holmén, Darrell B. Sonntag

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)435-443
Number of pages9
JournalTransportation Research Part D: Transport and Environment
Volume16
Issue number6
DOIs
StatePublished - Aug 2011

Keywords

  • Buses' gaseous emissions
  • Emissions modeling and forecasting
  • Particle number emissions

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation
  • Environmental Science(all)

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