  Drivers have distinctively diverse behaviors when operating vehicles in
natural traffic flow, such as preferred pedal position, car-following distance,
preview time headway, etc. These highly personalized behavioral variations are
known to impact vehicle fuel economy qualitatively. Nevertheless, the
quantitative relationship between driving behaviors and vehicle fuel
consumption remains obscure. Addressing this critical missing link will
contribute to the improvement of transportation sustainability, as well as
understanding drivers' behavioral diversity. This study proposed an integrated
microscopic driver behavior and fuel consumption model to assess and predict
vehicle fuel economy with naturalistic highway and local commuting traffic
data. Through extensive Monte Carlo simulations, significant correlation
results are revealed between specific individual driving preferences and fuel
economy over drivers' frequent commuting routes. Correlation results indicate
that the differences in fuel consumption incurred by various driving behaviors,
even in the same traffic conditions, can be as much as 29% for a light-duty
truck and 15% for a passenger car. A Gaussian Process Regression model is
further trained, validated, and tested under different traffic and vehicle
conditions to predict fuel consumption based on drivers' personalized
behaviors. Such a quantitative and personalized model can be used to identify
and recommend fuel-friendly driving behaviors and routes, demonstrating a
strong incentive for relevant stakeholders.
