Investigating Methods of Balancing Inequality and Efficiency in Ride Pooling

Authors

  • Naveen Raman University of Maryland, College Park

DOI:

https://doi.org/10.1609/aaai.v35i18.17985

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.

Downloads

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. https://doi.org/10.1609/aaai.v35i18.17985

Issue

Section

AAAI Undergraduate Consortium