Abstract
Most ride-sourcing platforms, exemplified by industry leaders like Uber, Lyft, and Didi, provide a range of ride services tailored to the diverse preferences of their passengers. Passengers, driven by their distinct priorities, may opt for high-class (HC) ride services, such as Luxury rides, if they value service quality, while those more cost-conscious may gravitate toward low-class (LC) ride services, including basic solo and shared rides. However, this market fragmentation can manifest as an excess of HC vehicles idly cruising the streets, while an insufficient number of LC vehicles struggle to meet passenger demand for LC services. To mitigate this issue, upgrading strategy is proposed where some LC vehicle requests are elevated to HC ride services without incurring additional charges. This study embarks on an initial exploration of the impacts of upgrading within the ride-sourcing system. We develop a mathematical model to depict the equilibrium conditions and analyze the collective influence of operational strategies, encompassing upgrading, spatial pricing, and vehicle repositioning, on system performances. Our research identifies scenarios in which the platform should employ these strategies to balance supply and demand and curb superfluous idle vehicle movements, supported by both theoretical and numerical analyses. The results offer operational insights that guide platform decisions, allowing them to adapt their strategies effectively in response to various supply–demand dynamics.
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