Date of Award
Master of Science
Civil, Construction, and Environmental Engineering
Passenger Transportation is one of the two major components of transportation sector (the other being freight) and it is one of the major factors affecting the energy demand and the need for transportation infrastructure investments. Specifically, 12% of energy consumption and almost 17% of total greenhouse gas emissions in the United States are attributed to passenger transportation, while the energy consumption due to passenger transportation is almost 60% of the total energy consumption in transportation sector. These statistics indicate the importance of predicting passenger transportation for future energy and transportation infrastructure investment planning. Vehicle Miles Traveled (VMT) is one of the most common measure estimating passenger trips in the United States and has been traditionally used to determine the need for new infrastructure. As the availability of energy resources and the funding for new infrastructure decrease, the need of forecasting VMT in the future for energy and transportation investment planning becomes vital.
Various studies in the past have determined the factors affecting VMT. Demographic and socioeconomic characteristics, road infrastructure, and land use influence the amount of passenger trips, but also fuel prices and government policy. Increase of population and income per capita has been traditionally the factor resulting directly to the increase of VMT while areas with higher density result to lower per capita single vehicle travel demand. Moreover, the increase of fuel cost decreases VMT while the impact of lane miles is totally opposite.
While previous studies have investigated the effect of demographic and socioeconomic characteristics, or the effect of land use and road capacity, or the effect of fuel prices on VMT, the effect of these factors has not been fully examined in a multivariate context. The objective of this thesis is to determine the factors that influence passenger trips and develop a prediction model of VMT in the future. Using panel data for the 48 continental states during the period 1998-2008, simultaneous equation models were developed for predicting VMT on different road functional classes and examining how new technology (telecommuting, alternative fuel vehicles) but also changes in fuel prices can affect the amount of passenger trips across the nation. Moreover, a panel data regression model with random coefficients was developed to identify the factors affecting total VMT. The use of panel data allows for the determination of the influence of different factors but also the effect of these factors across different states and years. To assess the influence of each significant factor on VMT, elasticities were estimated.
Further, the effect of innovations in technology (such as telecommuting and alternative fuel vehicles) and various government policies on energy consumption and greenhouse emissions was investigated. Different scenarios for high speed rail network, alternative fuel vehicle market share, fuel tax and density in the future were developed in order to quantify that impact. The estimation results of the model for total VMT were used to estimate the influence of each policy and scenario on the amount of total VMT, while the reduction of energy consumption and greenhouse gas emissions was estimated using the software VISION, developed by the Argonne National Laboratory.
The estimated models of passenger trips can assist transportation planners and policy-makers to determine the energy and transportation infrastructure investment needs in the future.
Key words: Passenger trips, energy consumption, infrastructure plan, policy, new technology.
Rentziou, Aikaterini, "Predicting passenger trips for future energy and transportation investment planning" (2010). Graduate Theses and Dissertations. 11541.