驱动程序激增定价
Driver Surge Pricing
摘要：
像Uber和Lyft这样的叫车市场使用动态定价（通常称为激增）来平衡可用司机的供应与乘车需求。我们研究了这些市场的司机端支付机制，提出了Uber新的附加司机激增机制设计的理论基础。我们提出了一个动态随机模型来捕捉冲击定价对司机收入和他们的策略，以最大限度地提高这种收入的影响。在这种情况下，某些时间段（激增）比其他时间段（非激增）更有价值，因此不同时间长度的行程在诱导驾驶员机会成本方面有所不同。首先，我们表明，乘性激增，历史上的标准乘车平台，是不激励兼容的动态设置。然后，我们提出了一个结构化的，激励兼容的定价机制。这种封闭形式的机制具有简单的形式，并且很好地近似于Uber的新添加剂浪涌机制。最后，通过数值分析和来自网约车市场的真实数据，我们证明了加法激增在实践中比乘法激增更具有激励相容性。
英文摘要：
Ride-hailing marketplaces like Uber and Lyft use dynamic pricing, often called surge, to balance the supply of available drivers with the demand for rides. We study driver-side payment mechanisms for such marketplaces, presenting the theoretical foundation that has informed the design of Uber's new additive driver surge mechanism. We present a dynamic stochastic model to capture the impact of surge pricing on driver earnings and their strategies to maximize such earnings. In this setting, some time periods (surge) are more valuable than others (non-surge), and so trips of different time lengths vary in the induced driver opportunity cost. First, we show that multiplicative surge, historically the standard on ride-hailing platforms, is not incentive compatible in a dynamic setting. We then propose a structured, incentive-compatible pricing mechanism. This closed-form mechanism has a simple form and is well-approximated by Uber's new additive surge mechanism. Finally, through both numerical analysis and real data from a ride-hailing marketplace, we show that additive surge is more incentive compatible in practice than is multiplicative surge.
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