Truck Fuel Research Articles

Truck Fuel Consumption Prediction Using Logistic Regression and Artificial Neural Networks

Rising international oil costs and the transport industry's recovery from the effects of Covid-19 resulted in the efficient management of fuel by logistics companies becoming a significant concern. One way of managing this is by analyzing the fuel consumption of trucks so as to better utilize the costly resource. Twenty-three driving data variables were gathered from 210 freight trucks and analyzed this data. Relevant variables that impact truck fuel consumption were extracted from the initial 23 variables gathered using stepwise regression, and then a prediction model was built from the identified relevant variables utilizing a binary logistic regression model. In addition, a back propagation neural network was employed in this study to create a second model of truck fuel use, and comparisons between the two models were made. The outcomes showed that the binary logistic regression model and the back-propagated neural network model prediction accuracy were 68.4% and 77.2%, respectively.

Effects of Road Slope and Vehicle Weight on Truck Fuel Consumption

In this paper, we developed truck fuel consumption models for the particular assistance of professionals in charge of road project valuation in terms of predicting fuel used by trucks, which is an important topic on vehicle operating costs to be considered in the benefit–cost analysis of road projects. On the other hand, fuel consumption has a direct impact on emissions to the atmosphere, and thus future research can be conducted regarding estimations about emissions by trucks. In this research, we identified the effect of overall vehicle weight on truck fuel consumption in a free-flow regime. The methodology includes the design of experiments and factorial design as statistical techniques to obtain data, as well as linear and non-linear regressions to obtain models for two types of trucks: rigid (three axles) and articulated (six axles). Notably, there is no evidence of research previously conducted on the latter. We used statistical methods for the selection of trucks, equipment, road segments, and other aspects, obtaining good control in tests verifying the appropriate values for factors according to the planned ones. The results satisfy the expectations of the research, and it was demonstrated that the vehicle weight and roadway slope were significantly more important than speed alone, which was typically considered the main variable in other studies. On the other hand, longitudinal slopes higher than 5% were found to not be suitable for freight road corridors. It is recommended that 6-axle trucks instead of 3-axle trucks be used for a 16 t amount of cargo transported on a plain road (longitudinal slope under 3%). The HDM-4 model did not represent fuel consumption adequately for the current vehicle fleet operating on roads. Fuel consumption models must be updated, for instance, every 10 years, such that they can adapt to vehicle technological advances and the energetic improvement of fuels, including the proportion of biofuels and gas.

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

AbstractIn 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.

Developed Prototype of Truck Fuel Measurement System to Prevent Fraudulent Receipt of Oil Subsidy

Developed Prototype of Truck Fuel Measurement System to Prevent Fraudulent Receipt of Oil Subsidy

Analysis Process for Truck Fuel Efficiency Study

Analysis Process for Truck Fuel Efficiency Study

Heavy Duty Truck Fuel Consumption Prediction Based on Driving Cycle Properties

ABSTRACT A methodology originally developed to predict vehicle emissions was applied to prediction of fuel consumption for 56 over-the-road heavy-duty trucks recruited in southern California. The method employed measurements exercised over chassis dynamometer cycles and the properties of those cycles. Nine driving cycle properties and their combinations were used to predict fuel consumption over an “unseen” cycle, based on measurements from up to four different baseline driving cycles. The results showed that the use of average velocity and average positive acceleration was suitable for the translation of fuel consumption between cycles, producing the lowest prediction error among the cases considered.

Judicial Review of Class Action Settlements

Judicial Review of Class Action Settlements