Engineering and Computer Science Faculty Publications
Dynamic Eco-Driving's Fuel Saving Potential in Traffic: Multi-Vehicle Simulation Study Comparing Three Representative Methods
Document Type
Article
Publication Date
11-21-2017
Journal Title
IEEE Transactions on Intelligent Transportation Systems
Volume
PP
Issue
99
First Page
1
Last Page
9
DOI
https://doi.org/10.1109/TITS.2017.2766767
Abstract
Dynamic eco-driving is a well-known umbrella term describing speed control schemes that utilize connected and automated vehicle technology for the purpose of saving fuel. If dynamic eco-driving is to be widely prescribed as an integral part of widespread fuel-saving endeavors, its expected performance as part of the overall traffic system must be analyzed. Specifically, it must be determined to what extent this type of control remains effective in the presence of dense traffic. This paper presents a series of multi-vehicle traffic simulations, which begin to answer important questions surrounding the effects of dynamic eco-driving on traffic and its potential for fuel savings in a mixed traffic environment. Three representative methods of dynamic eco-driving are tested in various high traffic scenarios and the estimated fuel economy, trip time, and average speed results are compared. Independent variables include technology penetration rate and amount of traffic, quantified by the delay level of service of the road network's traffic light facility. It is shown that, for the given test cases, average mpg increases linearly with technology penetration rate and dynamic eco-driving causes an average increase in mpg regardless of traffic amount. Overall results are promising for the usefulness of this clever class of fuel-saving technologies, in high traffic as well as low.
Keywords
Fuel economy, automated highways, intelligent vehicles, connected vehicles, traffic simulation, eco-driving
Recommended Citation
Fredette, Danielle and Özguner, Ümit, "Dynamic Eco-Driving's Fuel Saving Potential in Traffic: Multi-Vehicle Simulation Study Comparing Three Representative Methods" (2017). Engineering and Computer Science Faculty Publications. 352.
https://digitalcommons.cedarville.edu/engineering_and_computer_science_publications/352