Stochastic traffic capacity is used in traffic modelling and control for unidirectional sections of road infrastructure, although some of the estimation methods have recently proved flawed. However, even sound estimation methods require sufficient data. Because breakdowns are rare, the number of recorded breakdowns effectively determines sample size. This is especially relevant for temporary traffic infrastructure, but also for permanent bottlenecks (e.g., on- and off-ramps), where practitioners must know when estimates are reliable enough for control or design decisions. This paper studies this reliability along with the impact of censored data using synthetic data with a known capacity distribution. A corrected maximum-likelihood estimator is applied to varied samples. In total, 360 artificial measurements are created and used to estimate the capacity distribution, and the deviation from the pre-defined distribution is then quantified. Results indicate that at least 50 recorded breakdowns are necessary; 100-200 are the recommended minimum for temporary measurements. Beyond this, further improvements are marginal, with the expected average relative error below 5 %.

翻译：随机交通容量在道路交通基础设施单向路段的交通建模与控制中得到应用，尽管近期研究表明部分估计方法存在缺陷。然而，即使是合理的估计方法也需要充足的数据支撑。由于交通崩溃事件较为罕见，实际记录的崩溃次数本质上决定了样本规模。这一特性对于临时交通基础设施尤为重要，同时也适用于永久性瓶颈路段（例如匝道出入口），从业者必须明确何时获得的估计结果足以支撑控制或设计决策。本文基于已知容量分布的合成数据，研究了此类估计的可靠性及删失数据的影响。采用修正的最大似然估计器对多样本进行分析，共生成360组人工测量数据用于估计容量分布，进而量化其与预设分布的偏差。结果表明，至少需要50次记录的崩溃事件；对于临时性测量，建议最小样本量为100-200次。超过该阈值后，估计精度的提升趋于平缓，预期平均相对误差可降至5%以下。