Investigating Methods of Balancing Inequality and Efficiency in Ride Pooling

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.