Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles

In this research, by taking advantage of dynamic fuel consumption-speed data from Internet of Vehicles, we develop two novel computational approaches to more accurately estimate truck fuel consumption. The first approach is on the basis of a novel index, named energy consumption index, which is to explicitly reflect the dynamic relationship between truck fuel consumption and truck drivers' driving behaviors obtained from Internet of Vehicles. The second approach is based on a Generalized Regression Neural Network model to implicitly establish the same relationship. We further compare the two proposed models with three well-recognized existing models: vehicle specific power (VSP) model, Virginia Tech microscopic (VT-Micro) model, and Comprehensive Modal Emission Model (CMEM). According to our validations at both microscopic and macroscopic levels, the two proposed models have stronger performed in predicting fuel consumption in new routes. The models can be used to design more energy-efficient driving behaviors in the soon-to-come era of connected and automated vehicles.