Highlights Influence of driving behavior on fuel efficiency is analyzed. Volatility used as a surrogate for identifying driving behavior. K-means, K-medoids and hierarchical clustering used to classify driving behavior. Powertrain drive cycle simulations used to assess fuel efficiency and emissions. Aggressive driving observed to create 23% increase in fuel consumption.
Abstract Consumption of fossil fuel-based energy for vehicle propulsion and associated emissions are a global concern.One pathway to energy reduction is to examine situations where high-energy consumption occurs on roadways, e.g., speed volatility at work zones, or on sharp curves, which has been understudied. Harnessing second-by-second data from the naturalistic driving study and using the concept of driving volatility, this paper explores driving styles in work-zones and curves using machine learning approaches (k-medoids, hierarchical clustering) and drive-cycle simulations from Autonomie®. Results show that aggressive driving account for 12.2 % and 15.4 % of events that occurred in work zones and on curves and leads to 23 % increase in fuel consumption as opposed to normal driving.These results have implications for transportation agencies to improve work-zone configurations and provide signage or technology on curves to reduce fuel consumption and emissions. Moreover, automated and connected vehicles can smooth out traffic flow with advanced advisories and warnings.
Assessing driving behavior influence on fuel efficiency using machine-learning and drive-cycle simulations
2023-12-15
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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