High-speed passenger rail is seen by many in the U.S. transportation policy and planning communities as an ideal solution for fast, safe, and resource-efficient mobility in high-demand intercity corridors. To expand the body of knowledge for high-speed intercity passenger rail in the U.S., the overall goal of this dissertation was to better understand the demand for high-speed intercity passenger rail services in small- or medium-sized intermediate communities and improve planners' ability to estimate such demand through traveler surveys; specifically, the use of different experimental designs for stated preference questions and the use of images to describe hypothetical travel alternatives in traveler surveys. In pursuit of this goal, an Internet-based survey was distributed to residents of Waco and Temple, two communities located along the federally-designated South Central High-Speed Rail Corridor in Central Texas. A total of 1,160 surveys were obtained from residents of the two communities. Mixed logit travel mode choice models developed from the survey data revealed valuable findings that can inform demand estimates and the design of traveler surveys for high-speed intercity passenger rail planning activities. Based on the analysis presented in this dissertation, ridership estimates for new high-speed intercity passenger rail lines that are planned to serve intermediate communities should not assume that residents of these communities have similar characteristics and values. The d-efficient stated preference experimental design was found to provide a mode choice model with a better fit and greater significance on key policy variables than the adaptive design and therefore is recommended for use in future surveys. Finally, it is recommended that surveys should consider the use of images of proposed train services to aid respondent decision-making for stated preference questions, but only if the images used in the survey depict equipment that could be realistically deployed in the corridor.

xv, 173 p. : ill. (some col.) A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. / Economists typically assume that individuals behave in accordance with rational choice theory. In practice, however, individual behavior can deviate from the predictions of models founded upon basic economic theory. The extent to which these deviations are important to individual decision-making in environmental economics, and thus to the development of sound environmental policies, is not fully understood. The objective in this dissertation research is to investigate potential deviations from rational choice behavior in some environmental economics contexts and to identify their relevance to environmental policy. Chapter I uses a stated-preference survey for the valuation of environmental health-risk reductions in which respondents rate the subjective difficulty of each key choice they are asked to consider. Existing literature identifies many potential categories of biases in the empirically estimated valuation of non-market goods in stated-preference research. One potential source of bias stems from the "objective complexity" of the choice scenario. I find that existing objective measures of choice set complexity do not fully explain subjective choice difficulty ratings in this valuation survey. Instead, subjective difficulty appears to result from the interplay among objective complexity, preferences, and cognitive resource constraints. In Chapter II, I consider the possible consequences of choice difficulty from the standpoint of neuroeconomics. Within the scope of neuroeconomics, one can identify some neurobiological correlates of economic decision-making activity. I study the apparent effects of choice difficulty on the neurobiological encoding of individuals' value assessments. Information from this study provides a neurological basis for deviations from simple economic theory based on conventional models of rational choice. Chapter III examines risk perceptions that may influence individuals' decisions to migrate within the U.S. to reduce potential health and economic risks related to climate change. My analysis treats historical patterns of migration among counties as a function of varying spatial and temporal patterns in tornado activity, along with other spatially and temporally delineated variables intended to capture the evolution of subjective perceptions of these tornado risks. Results suggest that the perception of risk from extreme weather events can have a small but statistically discernible effect on migration behavior across sociodemographic groups for both out-migrants and in-migrants. / Committee in charge: Trudy Cameron, Chairperson, Economics; William Harbaugh, Member, Economics; Jason Lindo, Member, Economics; Ulrich Mayr, Outside Member, Psychology

This research examined if travelers are paying for travel on managed lanes (MLs) as they indicated that they would in a 2008 survey. The other objectives of this research included estimating travelers’ value of travel time savings (VTTS) and their value of travel time reliability (VOR), and examining the multiple survey designs used in a 2008 survey to identify which survey design better predicted ML traveler behavior. To achieve the objectives, an Internet-based follow-up stated preference (SP) survey of Houston’s Katy Freeway travelers was conducted in 2010. Three survey design methodologies—Db-efficient, random level generation, and adaptive random—were tested in this survey. A total of 3,325 responses were gathered from the survey, and of those, 869 responses were from those who likely also responded to the previous 2008 survey. Mixed logit models were developed for those 869 previous survey respondents to estimate and compare the VTTS to the 2008 survey estimates. It was found that the 2008 survey estimates of the VTTS were very close to the 2010 survey estimates. In addition, separate mixed logit models were developed from the responses obtained from the three different design strategies in the 2010 survey. The implied mean VTTS varied across the design-specific models. Only the Db-efficient design was able to estimate a VOR. Based on this and several other metrics, the Db-efficient design outperformed the other designs. A mixed logit model including all the responses from all three designs was also developed; the implied mean VTTS was estimated as 65 percent ($22/hr) of the mean hourly wage rate, and the implied mean VOR was estimated as 108 percent ($37/hr) of the mean hourly wage rate. Data on actual usage of the MLs were also collected. Based on actual usage, the average VTTS was calculated as $51/hr. However, the $51/hr travelers are paying likely also includes the value travelers place on travel time reliability of the MLs. The total (VTTS+VOR) amount estimated from the all-inclusive model from the survey was $59/hr, which is close to the value estimated from the actual usage. The Db-efficient design estimated this total as $50/hr. This research also shows that travelers have a difficulty in estimating the time they save while using a ML. They greatly overestimate the amount of time saved. It may well be that even though travelers are saving a small amount of time they value that time savings (and avoiding congestion) much higher – possibly similar to their amount of perceived travel time savings. The initial findings from this study, reported here, are consistent with the hypothesis that travelers are paying for their travel on MLs, much as they said that they would in our previous survey. This supports the use of data on intended behavior in policy analysis.