Date of Award:

5-2015

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Civil and Environmental Engineering

Committee Chair(s)

Kevin Heaslip

Committee

Kevin Heaslip

Committee

Paul Barr

Committee

Ryan Bosworth

Committee

Ziqi Song

Committee

Michael D. Thomas

Abstract

The overall objective of this research is to study the impacts of technology improvement including fuel efficiency increment, extending the use of natural gas vehicle and electric vehicles on key parameters of transportation. In the first chapter, a simple economic analysis is used in order to demonstrate the adoption rate of natural gas vehicles as an alternative fuel vehicle. The effect of different factors on adoption rate of commuters is calculated in sensitivity analysis.

In second chapter the VMT is modeled and forecasted under influence of CNG vehicles in different scenarios. The VMT modeling is based on the time series data for Washington State. In order to investigate the effect of population growth on VMT, the per capita model is also developed.

In third chapter the effect of fuel efficiency improvement on fuel tax revenue and greenhouse emission is examined. The model is developed based on time series data of Washington State. The rebound effect resulted from fuel efficiency improvement is estimated and is considered in fuel consumption forecasting. The reduction in fuel tax revenue and greenhouse gas (GHG) emissions as two outcomes of lower fuel consumption are computed. In addition, the proper fuel tax rate to restitute the revenue is suggested.

In the fourth chapter effective factors on electric vehicles (EV) adoption is discussed. The constructed model is aggregated binomial logit share model that estimates the modal split between EV and conventional vehicles for different states over time. Various factors are incorporated in the utility function as explanatory variables in order to quantify their effect on EV adoption choices. The explanatory variables include income, VMT, electricity price, gasoline price, urban area and number of EV stations.

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