Truck platooning facilitates the operation of trucks in close proximity to one
another, resulting in decreased air resistance and improved fuel efficiency.
While previous research has mostly focused on the effects of intra-distance on
fuel savings, this study aims to develop fuel savings performance functions
considering various truck platooning configurations. This article
comprehensively investigates the influence of different truck platoon
configurations on fuel savings. This analysis focuses on examining the impacts
of several variables including inter-vehicle distance, platoon speed, truck
weight, number of trucks in the platoon, and the truck’s distinctive design
characteristics. Data used in the analysis were collected from 10 different
field experiments. Three machine learning techniques—artificial neural networks
(ANN), extreme gradient boosting (XGBoost), and K-nearest neighbors
(KNN)—alongside the negative binomial regression model were employed. Upon
evaluation, the negative binomial regression model emerged as the most accurate,
boasting a prediction accuracy of 74%. This high-performing model was
subsequently leveraged to derive an equation for estimating fuel savings. The
results indicated that the truck platoon’s size is the most significant factor
affecting fuel efficiency. Specifically, the inclusion of additional trucks in
the platoon leads to substantial fuel savings. Moreover, as the platoon’s speed
increases, there is a noticeable increase in fuel savings. The design of the
truck plays a role: conventional trucks are more fuel efficient than cab-over
trucks. Lastly, the weight of the truck has a minor impact on the platoon’s fuel
efficiency. Overall, it is essential to consider multiple variables when
evaluating truck platoon arrangements for optimal fuel efficiency.
