[Re] Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling

Sarah De Boer, Radu Alexandru Cosma, Lukas Knobel, Yeskendir Koishekenov, Benjamin Shaffrey

Abstract: Our work attempts to verify two methods to mitigate forms of inequality in ride-pooling platforms proposed in the paper "Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling": (1) integrating fairness constraints into the objective functions and (2) redistributing income of drivers. We extend this paper by testing for robustness to a change in the neighbourhood selection process by using actual Manhattan neighbourhoods and we use corresponding demographic data to examine differences in service based on ethnicity.