Researchers Matteo Cederle, Saverio Bolognani, and Gian Antonio Susto from the University of Padova have proposed a novel approach to improve the fairness and efficiency of mobility-on-demand (MoD) services, such as ride-hailing platforms. Their work, titled “Fair and Efficient allocation of Mobility-on-Demand resources through a Karma Economy,” was published in the journal IEEE Transactions on Intelligent Transportation Systems.
The team acknowledges that while MoD systems have revolutionized urban transportation, they have also contributed to socio-economic inequalities due to factors like surge pricing. Existing fairness-aware frameworks often fail to account for the dynamic nature of user urgency, which can fluctuate based on system conditions and external factors. To address this, the researchers introduce a non-monetary, Karma-based mechanism that models endogenous urgency, allowing user time-sensitivity to evolve in response to real-world demands.
The proposed framework builds upon the principles of classical Karma economies, which are designed to promote fairness and efficiency in resource allocation. The researchers have developed a theoretical model that maintains these guarantees while incorporating a more realistic representation of user behavior. In a simulated MoD scenario, the framework demonstrated high levels of system efficiency and equitable resource allocation for users.
For the energy industry, this research could have practical applications in optimizing the allocation of resources for energy-on-demand services, such as electric vehicle charging or energy storage systems. By incorporating a Karma-based mechanism, energy providers could potentially improve fairness and efficiency in resource allocation, ensuring that users with varying levels of urgency are served appropriately. This approach could contribute to more equitable and sustainable energy solutions in the future.
This article is based on research available at arXiv.