Application of machine learning for fuel consumption modelling of trucks

Perrotta, Federico and Parry, Tony and Neves, Luís C. (2017) Application of machine learning for fuel consumption modelling of trucks. In: 2017 IEEE International Conference on Big Data, 11-14 Dec 2017, Boston, USA.

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Abstract

This paper presents the application of three Machine Learning techniques to fuel consumption modelling of articulated trucks for a large dataset. In particular, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) models have been developed for the purpose and their performance compared. Fleet managers use telematic data to monitor the performance of their fleets and take decisions regarding maintenance of the vehicles and training of their drivers. The data, which include fuel consumption, are collected by standard sensors (SAE J1939) for modern vehicles. Data regarding the characteristics of the road come from the Highways Agency Pavement Management System (HAPMS) of Highways England, the manager of the strategic road network in the UK. Together, these data can be used to develop a new fuel consumption model, which may help fleet managers in reviewing the existing vehicle routing decisions, based on road geometry. The model would also be useful for road managers to better understand the fuel consumption of road vehicles and the influence of road geometry. Ten-fold cross-validation has been performed to train the SVM, RF, and ANN models. Results of the study shows the feasibility of using telematic data together with the information in HAPMS for the purpose of modelling fuel consumption. The study also shows that although all the three methods make it possible to develop models with good precision, the RF slightly outperforms SVM and ANN giving higher R-squared, and lower error.

Item Type: Conference or Workshop Item (Paper)
Keywords: fuel consumption, machine learning, neural networks, random forests, support vector machine, truck fleet management
Schools/Departments: University of Nottingham, UK > Faculty of Engineering > Department of Civil Engineering
Related URLs:
Depositing User: Perrotta, Federico
Date Deposited: 28 Nov 2017 14:45
Last Modified: 13 Dec 2017 05:42
URI: http://eprints.nottingham.ac.uk/id/eprint/48393

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