  Intelligent Transportation Systems (ITS) rely on connected vehicle
applications to address real-world problems. Research is currently being
conducted to support safety, mobility and environmental applications. This
paper presents the DrivingStyles architecture, which adopts data mining
techniques and neural networks to analyze and generate a classification of
driving styles and fuel consumption based on driver characterization. In
particular, we have implemented an algorithm that is able to characterize the
degree of aggressiveness of each driver. We have also developed a methodology
to calculate, in real-time, the consumption and environmental impact of spark
ignition and diesel vehicles from a set of variables obtained from the
vehicle's Electronic Control Unit (ECU). In this paper, we demonstrate the
impact of the driving style on fuel consumption, as well as its correlation
with the greenhouse gas emissions generated by each vehicle. Overall, our
platform is able to assist drivers in correcting their bad driving habits,
while offering helpful tips to improve fuel economy and driving safety.
