Optimizing priority-based strategy in ride-sourcing markets: A quality-quantity tradeoff

The rapid growth of ride-sourcing platforms has revolutionized urban mobility, creating new challenges in balancing service quality with operational efficiency. This paper examines optimal priority strategies for ride-sourcing platforms in markets where customers value both driver availability and service quality. A game-theoretic model is developed to analyze how prioritizing high-quality drivers, those exceeding a service quality threshold, impacts platform profitability and social welfare. This study introduces a continuum of priority levels and incorporates inefficiencies arising from prioritization (e.g., increasing matching time), enabling a systematic analysis of the trade-off between service quality and supply utilization. Our results reveal that well-designed priority strategies can achieve Pareto improvements, benefiting platforms, customers and drivers (including non-prioritized ones). Optimal prioritization depends critically on market conditions. In supply-rationing markets, strong prioritization leverages excess capacity to enhance perceived service quality, while in demand-rationing markets, moderate prioritization is preferred to avoid worsening shortages. Compared with wage discrimination, prioritization dominates in market with sufficient supply by reallocating resources strategically and then enhancing perceived service quality, whereas wage adjustments remain superior in demand-rationing settings or when prioritization inefficiencies offset potential gains.