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

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Xu, Zhigang
Wei, Tao
Easa, Said
Zhao, Xiangmo
Qu, Xiaobo
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2018
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Abstract

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.

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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
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© 2018 Computer-Aided Civil and Infrastructure Engineering. This is the peer reviewed version of the following article: Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles, Computer-Aided Civil and Infrastructure Engineering, Volume 33, Issue 3, March 2018, Pages 209-219, which has been published in final form at 10.1111/mice.12344. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving (http://olabout.wiley.com/WileyCDA/Section/id-828039.html)
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Civil engineering
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