Investigating Methods of Balancing Inequality and Efficiency in Ride Pooling
Keywords:Applications Of AI, Deep Learning, Game Theory, Reinforcement Learning
AbstractOur 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.
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
AAAI Undergraduate Consortium