Queueing Versus Surge Pricing Mechanism: Efficiency, Equity, and Consumer Welfare

IF 0.1 4区 工程技术 Q4 ENGINEERING, MANUFACTURING Manufacturing Engineering Pub Date : 2020-09-24 DOI:10.2139/ssrn.3699134
Yueyang Zhong, Zhixi Wan, Z. Shen
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引用次数: 3

Abstract

At the end of 2017, the leading ride-hailing platform DiDi Express replaced the commonly used surge pricing mechanism with a queueing mechanism, called the virtual queueing mechanism, in a number of cities across China to mitigate public relations pressure. To explore the benefits of the virtual queueing mechanism over the surge pricing mechanism in terms of operational efficiency, equity, and consumer welfare, we build a model of a data-driven state-dependent open queueing network, which we show can be approximated by an M/M/s+M queueing model with balking and reneging. We derive closed-form expressions for multiple performance metrics, including response rate, demand satisfaction rate, gross merchandise value (GMV), consumer surplus, and degree of inequality. Our theoretical and numerical analyses show that: (1) the virtual queueing mechanism outperforms the surge pricing mechanism in terms of consumer surplus, but is not as advantageous to generate GMV; (2) with respect to the response rate and demand satisfaction rate, the virtual queueing mechanism performs increasingly better than the surge pricing mechanism as demand increases; (3) the virtual queueing mechanism contributes to a more equitable ridesharing system, in the sense that consumer surplus is distributed across different classes of riders in a more balanced way. Using real data, we conduct a case study in Beijing, which verifies all the aforementioned results. The case study further finds that the response and demand satisfaction rates are significantly correlated with driver idleness.
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排队与峰时定价机制:效率、公平与消费者福利
2017年底,领先的叫车平台滴滴快线在中国多个城市用排队机制(称为虚拟排队机制)取代了常用的高峰期定价机制,以缓解公关压力。为了探讨虚拟排队机制在运营效率、公平和消费者福利方面优于峰时定价机制,我们建立了一个数据驱动的状态依赖开放排队网络模型,并证明了该模型可以近似为一个带有犹豫和违约的M/M/s+M排队模型。我们推导了多个绩效指标的封闭形式表达式,包括响应率、需求满意度、总商品价值(GMV)、消费者剩余和不平等程度。理论和数值分析表明:(1)虚拟排队机制在消费者剩余方面优于峰时定价机制,但在产生GMV方面不如峰时定价机制有利;(2)在响应率和需求满意率方面,随着需求的增加,虚拟排队机制的表现越来越好于峰时定价机制;(3)虚拟排队机制使得消费者剩余更均衡地分配给不同阶层的乘客,从而使拼车系统更加公平。我们利用实际数据,在北京进行了一个案例研究,验证了上述所有结果。案例分析进一步发现,响应率和需求满意率与司机怠速显著相关。
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Manufacturing Engineering
Manufacturing Engineering 工程技术-工程:制造
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6-12 weeks
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