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A Reinforcement Learning Approach for Dynamic Rebalancing in Bike-Sharing System
Our work offers practical insights for operators and enriches the integration of reinforcement learning into dynamic rebalancing problems, paving the way for more intelligent and robust urban mobility solutions.
On the estimation of discrete choice models to capture irrational customer behaviors
Specifically, we show how to use partially-ranked preferences to efficiently model rational and irrational customer types from transaction data.
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