# Designing Dynamic Pricing for Bike-sharing Systems via Differentiable Agent-based Simulation

arXiv: https://arxiv.org/abs/2507.23344 · HTML: https://mlsanity.com/paper/2507.23344/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 9 of 9)

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- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [Differentiable Agent-based Epidemiology](https://mlsanity.com/paper/2207.09714/) (2022)
- [A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems](https://mlsanity.com/paper/1802.04592/) (2018)
- [Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates](https://mlsanity.com/paper/2010.06558/) (2020)
- [Generalized Gumbel-Softmax Gradient Estimator for Generic Discrete Random Variables](https://mlsanity.com/paper/2003.01847/) (2020)
- [Learning with Posterior Sampling for Revenue Management under Time-varying Demand](https://mlsanity.com/paper/2405.04910/) (2024)
- [Fully Data-driven but Interpretable Human Behavioural Modelling with Differentiable Discrete Choice Model](https://mlsanity.com/paper/2412.19403/) (2024)

## Abstract (arXiv)

Bike-sharing systems are emerging in various cities as a new ecofriendly transportation system. In these systems, spatiotemporally varying user demands lead to imbalanced inventory at bicycle stations, resulting in additional relocation costs. Therefore, it is essential to manage user demand through optimal dynamic pricing for the system. However, optimal pricing design for such a system is challenging because the system involves users with diverse backgrounds and their probabilistic choices. To address this problem, we develop a differentiable agent-based simulation to rapidly design dynamic pricing in bike-sharing systems, achieving balanced bicycle inventory despite spatiotemporally heterogeneous trips and probabilistic user decisions. We first validate our approach against conventional methods through numerical experiments involving 25 bicycle stations and five time slots, yielding 100 parameters. Compared to the conventional methods, our approach obtains a more accurate solution with a 73% to 78% reduction in loss while achieving more than a 100-fold increase in convergence speed. We further validate our approach on a large-scale urban bike-sharing system scenario involving 289 bicycle stations, resulting in a total of 1156 parameters. Through simulations using the obtained pricing policies, we confirm that these policies can naturally induce balanced inventory without any manual relocation. Additionally, we find that the cost of discounts to induce the balanced inventory can be minimized by setting appropriate initial conditions.