在移动服务中，时间变异性对用户有多坏？
How bad is time variability for users in mobility services? An economic framework under expected- and non-expected utility
AI总结：
本文通过预期效用框架量化时间变异性成本，提出COTV/COT的理论上界，为可靠性导向服务的定价与设计提供数据轻量的基准。
摘要：
时间变异性是移动服务的普遍特征，也是福利损失的主要来源。尽管文献已经量化了时间变异性的成本（COTV），但理论上仍不清楚在最坏情况下时间变异性有多糟糕。没有这样的基准，量化的时间变异性成本缺乏一个原则性的参考来评估它们是否具有经济意义。同时，这个基准对于交通评估、服务设计和定价的战略优先级至关重要，特别是在早期决策中，详细评估往往不可行。为填补这一空白，本文开发了一个预期效用（EU）框架来量化时间成本（COT）和COTV，建立了理论上的上界。对于具有二次效用的用户，我们证明COTV/COT ≤ 1/2 CV²，其中CV是服务时间的变异系数。对于泊松过程，这是一个常见的假设，这个界限简化为COTV/COT ≤ 1/2，这意味着随机服务的总成本最多是相同确定性服务的1.5倍。在更一般的情况下，该比率取决于三个可解释的因素：CV和用户第二和第三阶风险偏好，通过相对风险厌恶（RRA）和相对谨慎（RP）来捕捉。我们确定了RRA和RP的基准值，这些值描述了对均值、方差和偏度相关减少的偏好。我们的分析扩展到非EU框架，包括双理论和等级依赖效用，显示了关键的结构洞察力仍然稳健。通过量化时间变异性带来的成本和COTV/COT比率，本研究提供了一个数据轻量的基准，用于早期决策，并为用户对可靠性改进的支付意愿提供了原则性的上界，指导可靠性导向服务的定价和设计。
英文摘要：
Time variability is a pervasive feature of mobility services and a major source of welfare loss. Although literature has quantified the cost of time variability (COTV), it remains theoretically unclear how bad time variability can be in the worst case. Without such a benchmark, quantified variability costs lack a principled reference for assessing whether they are economically meaningful. Meanwhile, this benchmark is critical for strategic prioritization in transport appraisal, service design, and pricing -- particularly in early-stage decision making where detailed valuation is often infeasible. To fill this gap, this paper develops an expected utility (EU) framework to quantify the cost of time (COT) and COTV, establishing theoretical upper bounds on the ratio $COTV/COT$. For users with quadratic utility, we show $COTV/COT \le 1/2 CV^2$, where $CV$ is the coefficient of variation of service time. For Poisson processes, a common assumption, this bound simplifies to $COTV/COT \le 1/2$, implying the total cost of a stochastic service is at most 1.5 times that of an otherwise identical deterministic service. In more general settings, the ratio depends on three interpretable factors: $CV$ and users' second- and third-order risk preferences, captured by relative risk aversion (RRA) and relative prudence (RP). We identify benchmark values of RRA and RP that characterize preferences over mean-, variance-, and skewness-related reductions. Our analysis extends to non-EU frameworks, including dual theory and rank dependent utility, showing that key structural insights remain robust. By quantifying the cost induced by time variability and the $COTV/COT$ ratio, this study provides a data-light benchmark for early-stage decision making and a principled upper bound on users' willingness to pay for reliability improvements, informing the pricing and design of reliability-oriented services.
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