Altruistic Ride Sharing: A Community-Driven Approach to Short-Distance Mobility
This addresses urban sustainability and fairness issues for city commuters by offering a community-driven alternative to profit-driven ride-sharing, though it builds incrementally on existing ride-sharing and multi-agent methods.
The paper tackled urban mobility challenges like congestion and fuel consumption by introducing Altruistic Ride-Sharing (ARS), a decentralized, peer-to-peer framework using altruism points instead of money, which reduced travel distance and emissions, increased vehicle utilization, and promoted equitable participation in tests with New York City taxi data.
Urban mobility faces persistent challenges of congestion and fuel consumption, specifically when people choose a private, point-to-point commute option. Profit-driven ride-sharing platforms prioritize revenue over fairness and sustainability. This paper introduces Altruistic Ride-Sharing (ARS), a decentralized, peer-to-peer mobility framework where participants alternate between driver and rider roles based on altruism points rather than monetary incentives. The system integrates multi-agent reinforcement learning (MADDPG) for dynamic ride-matching, game-theoretic equilibrium guarantees for fairness, and a population model to sustain long-term balance. Using real-world New York City taxi data, we demonstrate that ARS reduces travel distance and emissions, increases vehicle utilization, and promotes equitable participation compared to both no-sharing and optimization-based baselines. These results establish ARS as a scalable, community-driven alternative to conventional ride-sharing, aligning individual behavior with collective urban sustainability goals.