Abstract
A major challenge for ridesharing platforms is to guarantee profit and fairness simultaneously, especially in the presence of misaligned incentives of drivers and riders. We focus on the dispatching-pricing problem to maximize the total revenue while keeping both drivers and riders satisfied. We study the computational complexity of the problem, provide a novel two-phased pricing solution with revenue and fairness guarantees, extend it to stochastic settings and develop a dynamic (a.k.a., learning-while-doing) algorithm that actively collects data to learn the demand distribution during the scheduling process. We also conduct extensive experiments to demonstrate the effectiveness of our algorithms.
Keywords
- Planning and Scheduling: Planning Algorithms
- Agent-based and Multi-agent Systems: Mechanism Design
- AI Ethics, Trust, Fairness: Fairness & Diversity
- Planning and Scheduling: Planning under Uncertainty
- Planning and Scheduling: Planning with Incomplete Information
