Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach

Chen, Yuche; Gonder, Jeffrey; Young, Stanley; Wood, Eric

doi:10.1016/j.tra.2017.10.012

Title: Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach

Abstract

Autonomous vehicles are drawing significant attention from governments, manufacturers and consumers. Experts predict them to be the primary means of transportation by the middle of this century. Recent literature shows that vehicle automation has the potential to alter traffic patterns, vehicle ownership, and land use, which may affect fuel consumption from the transportation sector. In this paper, we developed a data-rich analytical framework to quantify system-wide fuel impacts of automation in the United States by integrating (1) a dynamic vehicle sales, stock, and usage model, (2) an historical transportation network-level vehicle miles traveled (VMT)/vehicle activity database, and (3) estimates of automation's impacts on fuel efficiency and travel demand. The vehicle model considers dynamics in vehicle fleet turnover and fuel efficiency improvements of conventional and advanced vehicle fleet. The network activity database contains VMT, free-flow speeds, and historical speeds of road links that can help us accurately identify fuel-savings opportunities of automation. Based on the model setup and assumptions, we found that the impacts of automation on fuel consumption are quite wide-ranging - with the potential to reduce fuel consumption by 45% in our 'Optimistic' case or increase it by 30% in our 'Pessimistic' case. Second, implementing automation on urban roadsmore »« less

Authors:

Chen, Yuche ^[1]; Gonder, Jeffrey ^[2]; Young, Stanley ^[2]; Wood, Eric ^[2]

Vanderbilt Univ., Nashville, TN (United States). Dept. of Civil and Environmental Engineering
National Renewable Energy Lab. (NREL), Golden, CO (United States)

Publication Date:: Mon Nov 06 00:00:00 EST 2017

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); USDOE

OSTI Identifier:: 1409303

Alternate Identifier(s):: OSTI ID: 1703812

Report Number(s):: NREL/JA-5400-68949
Journal ID: ISSN 0965-8564

Grant/Contract Number:: AC36-08GO28308

Resource Type:: Accepted Manuscript

Journal Name:: Transportation Research, Part A: Policy and Practice

Additional Journal Information:: Journal Volume: 107; Journal ID: ISSN 0965-8564

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 33 ADVANCED PROPULSION SYSTEMS; fuel consumption; autonomous vehicles; data-rich energy modeling

Citation Formats


                    Chen, Yuche, Gonder, Jeffrey, Young, Stanley, and Wood, Eric. Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach.  United States: N. p., 2017. 
Web.  doi:10.1016/j.tra.2017.10.012.

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                    Chen, Yuche, Gonder, Jeffrey, Young, Stanley, & Wood, Eric. Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach.  United States.  https://doi.org/10.1016/j.tra.2017.10.012

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                    Chen, Yuche, Gonder, Jeffrey, Young, Stanley, and Wood, Eric. Mon .  
"Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach".  United States.  https://doi.org/10.1016/j.tra.2017.10.012.  https://www.osti.gov/servlets/purl/1409303.

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@article{osti_1409303,

  title        = {Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach},

  author       = {Chen, Yuche and Gonder, Jeffrey and Young, Stanley and Wood, Eric},

  abstractNote = {Autonomous vehicles are drawing significant attention from governments, manufacturers and consumers. Experts predict them to be the primary means of transportation by the middle of this century. Recent literature shows that vehicle automation has the potential to alter traffic patterns, vehicle ownership, and land use, which may affect fuel consumption from the transportation sector. In this paper, we developed a data-rich analytical framework to quantify system-wide fuel impacts of automation in the United States by integrating (1) a dynamic vehicle sales, stock, and usage model, (2) an historical transportation network-level vehicle miles traveled (VMT)/vehicle activity database, and (3) estimates of automation's impacts on fuel efficiency and travel demand. The vehicle model considers dynamics in vehicle fleet turnover and fuel efficiency improvements of conventional and advanced vehicle fleet. The network activity database contains VMT, free-flow speeds, and historical speeds of road links that can help us accurately identify fuel-savings opportunities of automation. Based on the model setup and assumptions, we found that the impacts of automation on fuel consumption are quite wide-ranging - with the potential to reduce fuel consumption by 45% in our 'Optimistic' case or increase it by 30% in our 'Pessimistic' case. Second, implementing automation on urban roads could potentially result in larger fuel savings compared with highway automation because of the driving features of urban roads. Lastly, through scenario analysis, we showed that the proposed framework can be used for refined assessments as better data on vehicle-level fuel efficiency and travel demand impacts of automation become available.},

  doi          = {10.1016/j.tra.2017.10.012},

  journal      = {Transportation Research, Part A: Policy and Practice},

  number       = ,

  volume       = 107,

  place        = {United States},

  year         = {Mon Nov 06 00:00:00 EST 2017},

  month        = {Mon Nov 06 00:00:00 EST 2017}

}

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Fig. 1: Flowchart of the model.

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