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Title: Green Routing Fuel Saving Opportunity Assessment: A Case Study on California Large-Scale Real-World Travel Data: Preprint

Abstract

New technologies, such as connected and automated vehicles, have attracted more and more researchers for improving the energy efficiency and environmental impact of current transportation systems. The green routing strategy instructs a vehicle to select the most fuel-efficient route before the vehicle departs. It benefits the current transportation system with fuel saving opportunity through identifying the greenest route. This paper introduces an evaluation framework for estimating benefits of green routing based on large-scale, real-world travel data. The framework has the capability to quantify fuel savings by estimating the fuel consumption of actual routes and comparing to routes procured by navigation systems. A route-based fuel consumption estimation model, considering road traffic conditions, functional class, and road grade is proposed and used in the framework. An experiment using a large-scale data set from the California Household Travel Survey global positioning system trajectory data base indicates that 31% of actual routes have fuel savings potential with a cumulative estimated fuel savings of 12%.

Authors:
; ; ;
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:
1371835
Report Number(s):
NREL/CP-5400-68194
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 28th IEEE Intelligent Vehicles Symposium, 11-14 June 2017, Redondo Beach, California
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; green routing; fuel saving; case study; California Household Travel Survey; travel data; fuel efficient

Citation Formats

Zhu, Lei, Holden, Jacob, Gonder, Jeff, and Wood, Eric. Green Routing Fuel Saving Opportunity Assessment: A Case Study on California Large-Scale Real-World Travel Data: Preprint. United States: N. p., 2017. Web.
Zhu, Lei, Holden, Jacob, Gonder, Jeff, & Wood, Eric. Green Routing Fuel Saving Opportunity Assessment: A Case Study on California Large-Scale Real-World Travel Data: Preprint. United States.
Zhu, Lei, Holden, Jacob, Gonder, Jeff, and Wood, Eric. Thu . "Green Routing Fuel Saving Opportunity Assessment: A Case Study on California Large-Scale Real-World Travel Data: Preprint". United States. doi:. https://www.osti.gov/servlets/purl/1371835.
@article{osti_1371835,
title = {Green Routing Fuel Saving Opportunity Assessment: A Case Study on California Large-Scale Real-World Travel Data: Preprint},
author = {Zhu, Lei and Holden, Jacob and Gonder, Jeff and Wood, Eric},
abstractNote = {New technologies, such as connected and automated vehicles, have attracted more and more researchers for improving the energy efficiency and environmental impact of current transportation systems. The green routing strategy instructs a vehicle to select the most fuel-efficient route before the vehicle departs. It benefits the current transportation system with fuel saving opportunity through identifying the greenest route. This paper introduces an evaluation framework for estimating benefits of green routing based on large-scale, real-world travel data. The framework has the capability to quantify fuel savings by estimating the fuel consumption of actual routes and comparing to routes procured by navigation systems. A route-based fuel consumption estimation model, considering road traffic conditions, functional class, and road grade is proposed and used in the framework. An experiment using a large-scale data set from the California Household Travel Survey global positioning system trajectory data base indicates that 31% of actual routes have fuel savings potential with a cumulative estimated fuel savings of 12%.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jul 13 00:00:00 EDT 2017},
month = {Thu Jul 13 00:00:00 EDT 2017}
}

Conference:
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