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Title: Green Routing Fuel-Saving Opportunity Assessment: A Case Study Using Large-Scale, Real-World Travel Data

Abstract

This poster focuses on the National Renewable Energy Laboratory's assessment of the fuel-saving potential of green routing using a large-scale, real-world travel data set from California.

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:
1369130
Report Number(s):
NREL/PO-5400-68643
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2017 IEEE Intelligent Vehicles Symposium, 11-14 June 2017, Redondo Beach, California
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; real-world travel data; green routing; fuel saving; energy efficiency; greenhouse gas emissions; fuel consumption

Citation Formats

Zhu, Lei, Holden, Jacob, Wood, Eric, and Gonder, Jeffrey. Green Routing Fuel-Saving Opportunity Assessment: A Case Study Using Large-Scale, Real-World Travel Data. United States: N. p., 2017. Web. doi:10.1109/IVS.2017.7995882.
Zhu, Lei, Holden, Jacob, Wood, Eric, & Gonder, Jeffrey. Green Routing Fuel-Saving Opportunity Assessment: A Case Study Using Large-Scale, Real-World Travel Data. United States. doi:10.1109/IVS.2017.7995882.
Zhu, Lei, Holden, Jacob, Wood, Eric, and Gonder, Jeffrey. Sat . "Green Routing Fuel-Saving Opportunity Assessment: A Case Study Using Large-Scale, Real-World Travel Data". United States. doi:10.1109/IVS.2017.7995882. https://www.osti.gov/servlets/purl/1369130.
@article{osti_1369130,
title = {Green Routing Fuel-Saving Opportunity Assessment: A Case Study Using Large-Scale, Real-World Travel Data},
author = {Zhu, Lei and Holden, Jacob and Wood, Eric and Gonder, Jeffrey},
abstractNote = {This poster focuses on the National Renewable Energy Laboratory's assessment of the fuel-saving potential of green routing using a large-scale, real-world travel data set from California.},
doi = {10.1109/IVS.2017.7995882},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Jun 03 00:00:00 EDT 2017},
month = {Sat Jun 03 00:00:00 EDT 2017}
}

Conference:
Other availability
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  • 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 navigationmore » 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%.« less
  • 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 navigationmore » 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%.« less
  • The green routing strategy instructing a vehicle to select a fuel-efficient route benefits the current transportation system with fuel-saving opportunities. This paper introduces a navigation API route fuel-saving evaluation framework for estimating fuel advantages of alternative API routes based on large-scale, real-world travel data for conventional vehicles (CVs) and hybrid electric vehicles (HEVs). The navigation APIs, such Google Directions API, integrate traffic conditions and provide feasible alternative routes for origin-destination pairs. This paper develops two link-based fuel-consumption models stratified by link-level speed, road grade, and functional class (local/non-local), one for CVs and the other for HEVs. The link-based fuel-consumption modelsmore » are built by assigning travel from a large number of GPS driving traces to the links in TomTom MultiNet as the underlying road network layer and road grade data from a U.S. Geological Survey elevation data set. Fuel consumption on a link is calculated by the proposed fuel consumption model. This paper envisions two kinds of applications: 1) identifying alternate routes that save fuel, and 2) quantifying the potential fuel savings for large amounts of travel. An experiment based on a large-scale California Household Travel Survey GPS trajectory data set is conducted. The fuel consumption and savings of CVs and HEVs are investigated. At the same time, the trade-off between fuel saving and time saving for choosing different routes is also examined for both powertrains.« less
  • A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages nearly 1 million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rates look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to almost any typemore » of driving data. The model has been trained on vehicle global positioning system data from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. The estimation model has demonstrated an error range of 8.6% to 13.8%. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.« less
  • Highlights opportunities using GPS travel survey techniques and systems simulation tools for plug-in hybrid vehicle design improvements, which maximize the benefits of energy efficiency technologies.