# Altruistic Ride Sharing: A Framework for Fair and Sustainable Urban Mobility via Peer-to-Peer Incentives

arXiv: https://arxiv.org/abs/2510.13227 · HTML: https://mlsanity.com/paper/2510.13227/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 18 of 18)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1901.11454/) (2019)
- [DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03882/) (2019)
- [A Deep Value-network Based Approach for Multi-Driver Order Dispatching](https://mlsanity.com/paper/2106.04493/) (2021)
- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching](https://mlsanity.com/paper/1910.02591/) (2019)
- [A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning](https://mlsanity.com/paper/2010.01755/) (2020)
- [A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learning](https://mlsanity.com/paper/1910.14002/) (2019)
- [Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.10897/) (2020)
- [Travel Demand Forecasting: A Fair AI Approach](https://mlsanity.com/paper/2303.01692/) (2023)
- [Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems](https://mlsanity.com/paper/2112.00424/) (2021)
- [DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveries](https://mlsanity.com/paper/2311.02017/) (2023)
- [Revisiting the Gumbel-Softmax in MADDPG](https://mlsanity.com/paper/2302.11793/) (2023)
- [A Survey of Machine Learning-Based Ride-Hailing Planning](https://mlsanity.com/paper/2303.14646/) (2023)
- [HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control](https://mlsanity.com/paper/2304.03697/) (2023)
- [An algorithm for integrating peer-to-peer ridesharing and schedule-based transit system for first mile/last mile access](https://mlsanity.com/paper/2007.07488/) (2020)

## Abstract (arXiv)

Urban mobility systems face persistent challenges of congestion, underutilized vehicles, and rising emissions driven by private point-to-point commuting. Although ride-sharing platforms exist, their profit-driven incentive structures often fail to align individual participation with broader community benefit. We introduce Altruistic Ride Sharing (ARS), a decentralized peer-to-peer mobility framework in which commuters alternate between driver and rider roles using altruism points, a non-monetary credit mechanism that rewards providing rides and discourages persistent free-riding. To enable scalable coordination among agents, ARS formulates ride-sharing as a multi-agent reinforcement learning problem and introduces ORACLE (One-Network Actor-Critic for Learning in Cooperative Environments), a shared-parameter learning architecture for decentralized rider selection. We evaluate ARS using real-world New York City Taxi and Limousine Commission (TLC) trajectory data under varying agent populations and behavioral dynamics. Across simulations, ARS reduces total travel distance and associated carbon emissions by approximately 20%, reduces urban traffic density by up to 30%, and doubles vehicle utilization relative to no-sharing baselines while maintaining balanced participation across agents. These results demonstrate that altruism-based incentives combined with decentralized learning can provide a scalable and equitable alternative to profit-driven ride-sharing systems.