  Improving the fuel efficiency of vehicles is imperative to reduce costs and
protect the environment. While the efficient engine and vehicle designs, as
well as intelligent route planning, are well-known solutions to enhance the
fuel efficiency, research has also demonstrated that the adoption of
fuel-efficient driving behaviors could lead to further savings. In this work,
we propose a novel framework to promote fuel-efficient driving behaviors
through real-time automatic monitoring and driver feedback. In this framework,
a random-forest based classification model developed using historical data to
identifies fuel-inefficient driving behaviors. The classifier considers
driver-dependent parameters such as speed and acceleration/deceleration
pattern, as well as environmental parameters such as traffic, road topography,
and weather to evaluate the fuel efficiency of one-minute driving events. When
an inefficient driving action is detected, a fuzzy logic inference system is
used to determine what the driver should do to maintain fuel-efficient driving
behavior. The decided action is then conveyed to the driver via a smartphone in
a non-intrusive manner. Using a dataset from a long-distance bus, we
demonstrate that the proposed classification model yields an accuracy of 85.2%
while increasing the fuel efficiency up to 16.4%.
