Investigating Methods of Balancing Inequality and Efficiency in Ride Pooling

Authors

  • Naveen Raman University of Maryland, College Park

Keywords:

Applications Of AI, Deep Learning, Game Theory, Reinforcement Learning

Abstract

Our research focuses on developing matching policies that match drivers and riders for ride-pooling services. We aim to develop policies that balance efficiency and various forms of fairness. We did this through two methods: new matching algorithms that include a fairness term in the objective function, and income redistribution methods based on the Shapley value of a driver. I tested these methods on New York City Taxicab data to evaluate their performance and found that they succeed in reducing certain forms of fairness.

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Published

2021-05-18

How to Cite

Raman, N. (2021). Investigating Methods of Balancing Inequality and Efficiency in Ride Pooling. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15978-15979. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17985

Issue

Section

AAAI Undergraduate Consortium