Highlights ► Effects of different technological solutions and changes in fuel prices on passenger VMT by functional class and area. ► Effects of different policies governing fuel tax and population density on future energy consumption and GHG emissions. ► Significant heterogeneity in VMT of different population groups across states and over time. ► Non-uniform effects of fuel cost and fuel tax on VMT across functional classes of roads. ► Higher elasticity values of VMT with respect to urban lane miles than rural lane-miles.
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.
VMT, energy consumption, and GHG emissions forecasting for passenger transportation
Transportation Research Part A: Policy and Practice ; 46 , 3 ; 487-500
2011-11-29
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
VMT, energy consumption, and GHG emissions forecasting for passenger transportation
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