Incorporating driving behavior into vehicle fuel consumption prediction: methodology development and testing

This study is a signifcant endeavor involving the development and testing of a comprehensive methodology to incorporate driving behavior into the analysis and prediction of vehicle fuel consumption. It underscores the crucial importance
of understanding how diferent driving behavior afect fuel efciency. The framework we present is a theoretical construct
and a practical tool. It provides a robust, multi-step process for linking driving behavior to fuel consumption, leveraging
both traditional statistical methods and advanced machine learning techniques to derive actionable insights. To test
the framework, we used a naturalistic data that includes about 5408 diferent road users in a mixed trafc environment
and urban settings in Germany. We applied a microscopic fuel consumption model to calibrate the framework and an
unsupervised clustering algorithm to classify the behavior of the driver interacting with each other and with vulnerable
road users. The framework includes developing Linear regression model as a baseline, which yields an R-squared of 0.511
and a Mean Squared Error (MSE) of 0.031, indicating moderate predictive accuracy. The fnal step includes choosing
Random Forest as a better model, which achieves a higher R-squared of 0.956 and a lower MSE of 0.003. We also found
that conservative and aggressive driving leads to signifcantly higher and more discrepancy in fuel consumption than
normal driving behavior. These insights can promote more efcient driving practices, leading to signifcant fuel savings
and environmental benefts.