Towards Fair and Efficient allocation of Mobility-on-Demand resources through a Karma Economy

TL;DR

This paper proposes a Karma-based, non-monetary mechanism for fair and efficient resource allocation in mobility-on-demand systems, accounting for user urgency and behavior variability.

Contribution

It introduces a novel Karma economy framework that models endogenous user urgency, enhancing fairness and efficiency in mobility-on-demand resource allocation.

Findings

Demonstrates promising system efficiency and fairness in a simulated scenario.

Maintains classical Karma economy guarantees while modeling user behavior.

Highlights the potential for real-world application and future development.

Abstract

Mobility-on-demand systems like ride-hailing have transformed urban transportation, but they have also exacerbated socio-economic inequalities in access to these services, also due to surge pricing strategies. Although several fairness-aware frameworks have been proposed in smart mobility, they often overlook the temporal and situational variability of user urgency that shapes real-world transportation demands. This paper introduces a non-monetary, Karma-based mechanism that models endogenous urgency, allowing user time-sensitivity to evolve in response to system conditions as well as external factors. We develop a theoretical framework maintaining the efficiency and fairness guarantees of classical Karma economies, while accommodating this realistic user behavior modeling. Applied to a simplified simulated mobility-on-demand scenario, we provide a proof-of-concept illustration of the proposed framework, showing that it exhibits promising behavior in terms of system efficiency and equitable resource allocation, while acknowledging that a full treatment of realistic MoD complexity remains an important direction for future work.