Impact of Intelligent Transportation Systems on Vehicle Fuel Consumption and Emission Modeling: An Overview

Paper #:
  • 2013-01-9094

Published:
  • 2014-01-15
Citation:
Faris, W., Rakha, H., and Elmoselhy, S., "Impact of Intelligent Transportation Systems on Vehicle Fuel Consumption and Emission Modeling: An Overview," SAE Int. J. Mater. Manf. 7(1):129-146, 2014, https://doi.org/10.4271/2013-01-9094.
Pages:
18
Abstract:
Climate change due to greenhouse gas emissions has led to new vehicle emissions standards which in turn have led to a call for vehicle technologies to meet these standards. Modeling of vehicle fuel consumption and emissions emerged as an effective tool to help in developing and assessing such technologies, to help in predicting aggregate vehicle fuel consumption and emissions, and to complement traffic simulation models. The paper identifies the current state of the art on vehicle fuel consumption and emissions modeling and its utilization to test the environmental impact of the Intelligent Transportation Systems (ITS)’ measures and to evaluate transportation network improvements. The study presents the relevant models to ITS in the key classifications of models in this research area. It demonstrates that the trends of vehicle fuel consumption and emissions provided by current models generally do satisfactorily replicate field data trends. It shows as well that microscopic modeling is the most accurate type of vehicle fuel consumption and emissions modeling and macroscopic modeling is most helpful in estimating aggregate emissions inventories. In addition, the paper demonstrates that mesoscopic models and empirical models strike a balance between accuracy and simplicity so that they are highly suitable for evaluating the environmental impact of the ITS’ measures and transportation network improvements. The study indicates that there is a significant impact of ITS measures on vehicle fuel consumption and emissions rates. It identifies as well the relation among transportation models, cold start excess emission modeling subcategories, and hot-stabilized emission models. The study concludes that no one model as yet fully meets the needs of ITS applications.
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