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.

中文翻译：

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以多舱位服务升级网约车市场


大多数乘车采购平台（例如 Uber、Lyft 和滴滴出行等行业领导者）都提供一系列针对乘客不同偏好的乘车服务。如果乘客重视服务质量，则受其不同优先级的驱动，可能会选择高级 (HC) 乘车服务，例如豪华乘车，而那些更注重成本的乘客可能会倾向于低级 (LC) 乘车服务，包括基本的单人和共享游乐设施。然而，这种市场碎片化的表现是，HC 车辆过多在街道上闲逛，而 LC 车辆数量不足，难以满足乘客对 LC 服务的需求。为了缓解这个问题，提出了升级策略，将一些 LC 车辆请求提升为 HC 乘车服务，而不会产生额外费用。本研究初步探讨了乘车采购系统升级的影响。我们开发了一个数学模型来描述均衡条件并分析运营策略（包括升级、空间定价和车辆重新定位）对系统性能的集体影响。我们的研究确定了平台应采用这些策略来平衡供需并遏制多余的闲置车辆移动的场景，并得到理论和数值分析的支持。结果提供了指导平台决策的运营见解，使他们能够有效地调整策略以应对各种供需动态。