Browsing by Author "Travis, Laurel"

Now showing 1 - 3 of 3
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    Asymmetries in Potential for Partisan Gerrymandering
    Goedert, Nicholas; Hildebrand, Robert; Travis, Laurel; Pierson, Matthew (2024)
    This paper investigates the effectiveness of potential partisan gerrymandering of the U.S. House of Representatives across a range of states. We use a heuristic algorithm to generate district maps that optimize for multiple objectives, including compactness, partisan benefit, and competitiveness. While partisan gerrymandering is highly effective for both sides, we find that the majority of states are moderately biased toward Republicans when optimized for either compactness or partisan benefit, meaning that Republican gerrymanders have the potential to be more effective. However, we also find that more densely populated and more heavily Hispanic states show less Republican bias or even Democratic bias. Additionally, we find that in almost all cases we can generate reasonably compact maps with very little sacrifice to partisan objectives through a mixed objective function. This suggests that there is a strong potential for stealth partisan gerrymanders that are both compact and beneficial to one party. Nationwide, partisan gerrymandering is capable of swinging over one hundred seats in the U.S. House, even when compact districts are simultaneously sought.
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    Continuous Equality Knapsack with Probit-Style Objectives
    Fravel, Jamie; Hildebrand, Robert; Travis, Laurel (2022-11-04)
    We study continuous, equality knapsack problems with uniform separable, non-convex objective functions that are continuous, strictly increasing, antisymmetric about a point, and have concave and convex regions. For example, this model captures a simple allocation problem with the goal of optimizing an expected value where the objective is a sum of cumulative distribution functions of identically distributed normal distributions (i.e., a sum of inverse probit functions). We prove structural results of this model under general assumptions and provide two algorithms for efficient optimization: (1) running in linear time and (2) running in a constant number of operations given preprocessing of the objective function.
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    Demand and Capacity Problems in the Next Generation Air Transportation System
    Pu, Davide (Virginia Tech, 2015-01-23)
    This thesis investigates two main aspects of air transportation system, demand and capacity. The first study aims to estimate the potential market for Zip Vehicles, an advanced commuter type of aircraft equipped with automation and electric propulsion technologies. A Multinomial Logit Model was developed to estimate the mode choice behavior of commuters between Zip vehicle, auto and transit in seven metropolitan areas in the United States. The results showed that the Out-of-Vehicle travel time plays an important role in the decision process of commuters. Zip Vehicle is predicted to achieve residual demand with the current technologies and could become more competitive if it was equipped with Vertical Take-Off Technology. The second study developed a hybrid airport runway capacity model that blends both deterministic and simulation techniques. The model includes a graphic user interface that allows high degree of freedom to modify input parameters, such as airport information, weather conditions, minimum separation distances and aircraft grouping system. The model is widely validated and it appears to be a consistent solution for estimating airport capacity at different levels and with various degree of extensibility.