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

Abstract

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.