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Title: Data-driven fuel consumption estimation: A multivariate adaptive regression spline approach

Abstract

Providing guidance and information to drivers to help them make fuel-efficient route choices remains an important and effective strategy in the near term to reduce fuel consumption from the transportation sector. One key component in implementing this strategy is a fuel-consumption estimation model. In this paper, we developed a mesoscopic fuel consumption estimation model that can be implemented into an eco-routing system. Our proposed model presents a framework that utilizes large-scale, real-world driving data, clusters road links by free-flow speed and fits one statistical model for each of cluster. This model includes predicting variables that were rarely or never considered before, such as free-flow speed and number of lanes. We applied the model to a real-world driving data set based on a global positioning system travel survey in the Philadelphia-Camden-Trenton metropolitan area. Results from the statistical analyses indicate that the independent variables we chose influence the fuel consumption rates of vehicles. But the magnitude and direction of the influences are dependent on the type of road links, specifically free-flow speeds of links. Here, a statistical diagnostic is conducted to ensure the validity of the models and results. Although the real-world driving data we used to develop statistical relationships are specificmore » to one region, the framework we developed can be easily adjusted and used to explore the fuel consumption relationship in other regions.« less

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1378892
Report Number(s):
NREL/JA-5400-68860
Journal ID: ISSN 0968-090X
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Transportation Research Part C: Emerging Technologies
Additional Journal Information:
Journal Volume: 83; Journal Issue: C; Journal ID: ISSN 0968-090X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
30 DIRECT ENERGY CONVERSION; 33 ADVANCED PROPULSION SYSTEMS; data-driven analytics; fuel consumption estimation; multivariate adaptive regression spline; eco-routing

Citation Formats

Chen, Yuche, Zhu, Lei, Gonder, Jeffrey, Young, Stanley, and Walkowicz, Kevin. Data-driven fuel consumption estimation: A multivariate adaptive regression spline approach. United States: N. p., 2017. Web. doi:10.1016/j.trc.2017.08.003.
Chen, Yuche, Zhu, Lei, Gonder, Jeffrey, Young, Stanley, & Walkowicz, Kevin. Data-driven fuel consumption estimation: A multivariate adaptive regression spline approach. United States. doi:10.1016/j.trc.2017.08.003.
Chen, Yuche, Zhu, Lei, Gonder, Jeffrey, Young, Stanley, and Walkowicz, Kevin. Sat . "Data-driven fuel consumption estimation: A multivariate adaptive regression spline approach". United States. doi:10.1016/j.trc.2017.08.003. https://www.osti.gov/servlets/purl/1378892.
@article{osti_1378892,
title = {Data-driven fuel consumption estimation: A multivariate adaptive regression spline approach},
author = {Chen, Yuche and Zhu, Lei and Gonder, Jeffrey and Young, Stanley and Walkowicz, Kevin},
abstractNote = {Providing guidance and information to drivers to help them make fuel-efficient route choices remains an important and effective strategy in the near term to reduce fuel consumption from the transportation sector. One key component in implementing this strategy is a fuel-consumption estimation model. In this paper, we developed a mesoscopic fuel consumption estimation model that can be implemented into an eco-routing system. Our proposed model presents a framework that utilizes large-scale, real-world driving data, clusters road links by free-flow speed and fits one statistical model for each of cluster. This model includes predicting variables that were rarely or never considered before, such as free-flow speed and number of lanes. We applied the model to a real-world driving data set based on a global positioning system travel survey in the Philadelphia-Camden-Trenton metropolitan area. Results from the statistical analyses indicate that the independent variables we chose influence the fuel consumption rates of vehicles. But the magnitude and direction of the influences are dependent on the type of road links, specifically free-flow speeds of links. Here, a statistical diagnostic is conducted to ensure the validity of the models and results. Although the real-world driving data we used to develop statistical relationships are specific to one region, the framework we developed can be easily adjusted and used to explore the fuel consumption relationship in other regions.},
doi = {10.1016/j.trc.2017.08.003},
journal = {Transportation Research Part C: Emerging Technologies},
number = C,
volume = 83,
place = {United States},
year = {Sat Aug 12 00:00:00 EDT 2017},
month = {Sat Aug 12 00:00:00 EDT 2017}
}

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