Balancing Fairness and Profit in Rideshare using Deep Learning
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Rideshare services such as Uber and Lyft have become much more popular over the last few years. Determining which riders are allocated to which drivers is a challenging problem, complicated by the number of combinations of riders and drivers. Current solutions typically optimize for gross mean volume or profit, while ignoring fairness in driver pay and rider wait-time. We address these by taking a long-term view, using Neural Networks to simulate value functions, and use Markov Decision Processes to maximize fairness.