VMT, energy consumption, and GHG emissions forecasting for passenger transportation

Aikaterini Rentziou; Konstantina Gkritza; Reginald R. Souleyrette

doi:10.1016/j.tra.2011.11.009

VMT, energy consumption, and GHG emissions forecasting for passenger transportation

Aikaterini Rentziou, Konstantina Gkritza, Reginald R. Souleyrette

Civil Engineering

Research output: Contribution to journal › Article › peer-review

66 Scopus citations

Abstract

Globalization, greenhouse gas emissions and energy concerns, emerging vehicle technologies, and improved statistical modeling capabilities make the present moment an opportune time to revisit aggregate vehicle miles traveled (VMT), energy consumption, and greenhouse gas (GHG) emissions forecasting for passenger transportation. Using panel data for the 48 continental states during the period 1998-2008, the authors develop simultaneous equation models for predicting VMT on different road functional classes and examine how different technological solutions and changes in fuel prices can affect passenger VMT. Moreover, a random coefficient panel data model is developed to estimate the influence of various factors (such as demographics, socioeconomic variables, fuel tax, and capacity) on the total amount of passenger VMT in the United States. To assess the influence of each significant factor on VMT, elasticities are estimated. Further, the authors investigate the effect of different policies governing fuel tax and population density on future energy consumption and GHG emissions. The presented methodology and estimation results can assist transportation planners and policy-makers in determining future energy and transportation infrastructure investment needs.

Original language	English
Pages (from-to)	487-500
Number of pages	14
Journal	Transportation Research Part A: Policy and Practice
Volume	46
Issue number	3
DOIs	https://doi.org/10.1016/j.tra.2011.11.009
State	Published - Mar 2012

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 0835989. The authors would like to thank Anant Vyas of Argonne National Laboratory for his assistance with the VISION software, and Robert Rozycki of Federal Highway Administration for providing the HPMS data for this study.

Keywords

Energy consumption
GHG
Passenger transportation
Random parameters
SURE
VMT

ASJC Scopus subject areas

Civil and Structural Engineering
Transportation
Management Science and Operations Research

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.tra.2011.11.009

Cite this

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title = "VMT, energy consumption, and GHG emissions forecasting for passenger transportation",

abstract = "Globalization, greenhouse gas emissions and energy concerns, emerging vehicle technologies, and improved statistical modeling capabilities make the present moment an opportune time to revisit aggregate vehicle miles traveled (VMT), energy consumption, and greenhouse gas (GHG) emissions forecasting for passenger transportation. Using panel data for the 48 continental states during the period 1998-2008, the authors develop simultaneous equation models for predicting VMT on different road functional classes and examine how different technological solutions and changes in fuel prices can affect passenger VMT. Moreover, a random coefficient panel data model is developed to estimate the influence of various factors (such as demographics, socioeconomic variables, fuel tax, and capacity) on the total amount of passenger VMT in the United States. To assess the influence of each significant factor on VMT, elasticities are estimated. Further, the authors investigate the effect of different policies governing fuel tax and population density on future energy consumption and GHG emissions. The presented methodology and estimation results can assist transportation planners and policy-makers in determining future energy and transportation infrastructure investment needs.",

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author = "Aikaterini Rentziou and Konstantina Gkritza and Souleyrette, {Reginald R.}",

note = "Funding Information: This material is based upon work supported by the National Science Foundation under Grant No. 0835989. The authors would like to thank Anant Vyas of Argonne National Laboratory for his assistance with the VISION software, and Robert Rozycki of Federal Highway Administration for providing the HPMS data for this study. ",

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PY - 2012/3

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N2 - Globalization, greenhouse gas emissions and energy concerns, emerging vehicle technologies, and improved statistical modeling capabilities make the present moment an opportune time to revisit aggregate vehicle miles traveled (VMT), energy consumption, and greenhouse gas (GHG) emissions forecasting for passenger transportation. Using panel data for the 48 continental states during the period 1998-2008, the authors develop simultaneous equation models for predicting VMT on different road functional classes and examine how different technological solutions and changes in fuel prices can affect passenger VMT. Moreover, a random coefficient panel data model is developed to estimate the influence of various factors (such as demographics, socioeconomic variables, fuel tax, and capacity) on the total amount of passenger VMT in the United States. To assess the influence of each significant factor on VMT, elasticities are estimated. Further, the authors investigate the effect of different policies governing fuel tax and population density on future energy consumption and GHG emissions. The presented methodology and estimation results can assist transportation planners and policy-makers in determining future energy and transportation infrastructure investment needs.

AB - Globalization, greenhouse gas emissions and energy concerns, emerging vehicle technologies, and improved statistical modeling capabilities make the present moment an opportune time to revisit aggregate vehicle miles traveled (VMT), energy consumption, and greenhouse gas (GHG) emissions forecasting for passenger transportation. Using panel data for the 48 continental states during the period 1998-2008, the authors develop simultaneous equation models for predicting VMT on different road functional classes and examine how different technological solutions and changes in fuel prices can affect passenger VMT. Moreover, a random coefficient panel data model is developed to estimate the influence of various factors (such as demographics, socioeconomic variables, fuel tax, and capacity) on the total amount of passenger VMT in the United States. To assess the influence of each significant factor on VMT, elasticities are estimated. Further, the authors investigate the effect of different policies governing fuel tax and population density on future energy consumption and GHG emissions. The presented methodology and estimation results can assist transportation planners and policy-makers in determining future energy and transportation infrastructure investment needs.

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KW - Random parameters

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VMT, energy consumption, and GHG emissions forecasting for passenger transportation

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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