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

arXiv 2510.13227 ↗First posted 15 Oct 2025cs.MA cs.ET cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 15 Oct 2025.

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References · 18

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  3. 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 259
  4. 2019Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learningcited by 57
  5. 2019DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learningcited by 54
  6. 2021A Deep Value-network Based Approach for Multi-Driver Order Dispatchingcited by 45
  7. 2021Reinforcement Learning for Ridesharing: An Extended Surveycited by 35
  8. 2019Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matchingcited by 26
  9. 2020A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learningcited by 17
  10. 2019A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learningcited by 11
  11. 2020Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learningcited by 7
  12. 2023Travel Demand Forecasting: A Fair AI Approachcited by 7
6 more
  1. 2021Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systemscited by 6
  2. 2023DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveriescited by 4
  3. 2023A Survey of Machine Learning-Based Ride-Hailing Planningcited by 3
  4. 2023HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Controlcited by 3
  5. 2020An algorithm for integrating peer-to-peer ridesharing and schedule-based transit system for first mile/last mile accesscited by 2
  6. 2023Revisiting the Gumbel-Softmax in MADDPGcited by 2

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