DURO uses a graph-LSTM network to output demand prediction intervals, then solves a robust rebalancing model; in single-morning NYC simulations it reduces waiting times versus deterministic baselines and runs about 60 times faster than DRO.
How machine learning informs ride-hailing services: A survey,
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Robust Vehicle Rebalancing with Deep Uncertainty in Autonomous Mobility-on-Demand Systems
DURO uses a graph-LSTM network to output demand prediction intervals, then solves a robust rebalancing model; in single-morning NYC simulations it reduces waiting times versus deterministic baselines and runs about 60 times faster than DRO.