Modeling preference heterogeneity in uncertain choice contexts characterized by external shocks

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This dissertation consists of four papers that examine human behavior through the use of stated preference data and discrete choice models. The first paper investigates how preferences for cycling infrastructure are related to self-assessed health. Results show that individuals who consider themselves less healthy tend to prefer more protected bike lanes. Inexperienced cyclists also tend to prefer this kind of infrastructure, which indicates that such lanes can promote cycling among those who do not cycle frequently and those who would benefit more from the associated physical activity. The second paper evaluates the demand for a first-mile/last-mile microtransit service. We found that certain demographic groups, such as men, younger or Black respondents, those with more education, and those who use public transit more often, tend to be more interested in microtransit. We also found that respondents valued time on their current modes similarly to microtransit, while walking and waiting were burdensome. This shows that microtransit has the potential to draw demand, especially when it is designed to reduce access time. The third paper uses discrete choice models to understand preferences for COVID-19 testing. Results indicate a preference for faster and less invasive tests, as well as a mild preference for at-home and appointment-based testing. We also found that respondents tended to underestimated low probabilities of false diagnosis and overestimate medium ones. Finally, we found that people were willing to pay around four times more to avoid a false positive than a negative result, which suggests there is some degree of "willful ignorance" in the sample. The fourth paper investigates changes in New Yorkers' values of time on shared transportation modes during and after the COVID-19 pandemic as a function of crowding and masking. Results suggest that crowding will become less salient after the pandemic, and that, at the time data were collected, safety measures like masking or vaccination requirements were not highly valued. On the other hand, respondents were willing to pay to avoid carbon emissions, which suggests that there has been a shift of concern from the pandemic to the climate crisis. Overall, this dissertation proposes and applies methods that can be useful for choice modelers that want to explain or predict heterogeneous behaviors. Moreover, the findings presented here have practical implications for policymakers and practitioners looking to abate the climate crisis and understand behaviors during a future health crisis.

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194 pages


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COVID-19; discrete choice modeling; Markov chain Monte Carlo; travel behavior; value of time


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Union Local


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Committee Chair

Alvarez Daziano, Ricardo

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Committee Member

Samaranayake, Samitha
Klein, Nicholas

Degree Discipline

Systems Engineering

Degree Name

Ph. D., Systems Engineering

Degree Level

Doctor of Philosophy

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Government Document




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Attribution-ShareAlike 4.0 International


dissertation or thesis

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