Applying differentiable agent-based simulation to bike-sharing pricing yields dynamic discounts that balance station inventories with a 73-78% loss reduction and 100x faster convergence than differential evolution and finite differences in synthetic scenarios.
The bike sharing rebalancing problem with stochastic demands
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Designing Dynamic Pricing for Bike-sharing Systems via Differentiable Agent-based Simulation
Applying differentiable agent-based simulation to bike-sharing pricing yields dynamic discounts that balance station inventories with a 73-78% loss reduction and 100x faster convergence than differential evolution and finite differences in synthetic scenarios.