Altruistic Ride Sharing: A Community-Driven Approach to Short-Distance Mobility

2025-10-15

AI Summary

Urban traffic congestion and high carbon emissions stem from car-centric commuting patterns and profit-driven ride-sharing platforms that neglect fairness and sustainability. To address this, we propose a decentralized, altruism-oriented shared mobility framework that eliminates monetary incentives and instead introduces role rotation and an altruism-point system. Our approach integrates multi-agent deep deterministic policy gradient (MADDPG) reinforcement learning, game-theoretic equilibrium modeling, and population dynamics to enable dynamic ride matching and sustain long-term user participation—without centralized platform intervention. Empirical evaluation on New York City taxi trip data demonstrates that our framework significantly reduces total vehicle miles traveled (−28.3%) and carbon emissions (−26.7%), increases vehicle utilization (+41.2%), and enhances participation fairness, as evidenced by a 0.19 reduction in the Gini coefficient—outperforming both baseline no-sharing and state-of-the-art optimized sharing approaches.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Fair Division

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

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.

Problem

• Addressing urban congestion and fuel consumption from private commutes
• Replacing profit-driven ride-sharing with altruistic point-based incentives
• Ensuring fairness and sustainability through decentralized peer-to-peer framework

Innovation

• Decentralized peer-to-peer mobility framework using altruism points
• Multi-agent reinforcement learning for dynamic ride-matching
• Game-theoretic equilibrium guarantees for fair participation