Office of Undergraduate Research

Permanent URI for this communityhttp://hdl.handle.net/1903/20157

Emphasizing equitable and inclusive access to research opportunities, the University of Maryland's Office of Undergraduate Research (OUR) empowers undergraduates and faculty to engage and succeed in inquiry, creative activity, and scholarship. This collection includes materials shared by undergraduate researchers during OUR events. It also encompasses materials from Undergraduate Research Day 2020, Undergraduate Research Day 2021, and Undergraduate Research Day 2022, which were organized by the Maryland Center for Undergraduate Research.

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Author: search.filters.author.Raman, NaveenSubject: search.filters.subject.CMNS

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    Data Driven Methods to Balance Fairness and Profit in Ride-Pooling
    (2021-04) Raman, Naveen; Dickerson, John
    Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers. However, these platforms can induce inequality either through an unequal income distribution or disparate treatment of riders. We investigate two methods to reduce forms of inequality in ride-pooling platforms: (1) incorporating fairness constraints into the objective function and (2) redistributing income to drivers to reduce income fluctuation and inequality. To evaluate our solutions, we use the New York City taxi data set. For the first method, we find that optimizing for driver-side fairness outperforms state-of-the-art models on the number of riders serviced, both in the worst-off neighborhood and overall, showing that optimizing for fairness can assist profitability in certain circumstances. For the second method, we explore income redistribution as a way to combat income inequality by having drivers keep an r-fraction of their income, and contributing the rest to a redistribution pool. For certain values of r, most drivers earn near their Shapley value, while still incentivizing drivers to maximize value, thereby avoiding the free-rider problem while reducing income variability.
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    Balancing Fairness and Profit in Rideshare using Deep Learning
    (2020-04-26) Raman, Naveen; Dickerson, John
    Rideshare services such as Uber and Lyft have become much more popular over the last few years. Determining which riders are allocated to which drivers is a challenging problem, complicated by the number of combinations of riders and drivers. Current solutions typically optimize for gross mean volume or profit, while ignoring fairness in driver pay and rider wait-time. We address these by taking a long-term view, using Neural Networks to simulate value functions, and use Markov Decision Processes to maximize fairness.