[论文解读] Reliability of stochastic capacity estimates

Igor Mikolášek|arXiv (Cornell University)|Feb 22, 2026

Traffic control and management被引用 0

一句话总结

论文通过合成数据（具有已知容量分布），使用经过校正的极大似然估计器，评估需要多少次故障事件才能可靠估计随机容量。

ABSTRACT

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 %.

研究动机与目标

• 评估单向道路段的随机容量估计的可靠性。
• 检查截断数据和样本量对估计精度的影响。
• 量化达到可接受估计误差所需的样本量。
• 在具有已知容量分布的合成数据上评估经过校正的最大似然估计器。

提出的方法

• 从已知容量分布生成360个人工测量值。
• 对不同样本应用经过校正的最大似然估计器。
• 量化各样本对预定义分布的偏离。
• 评估样本量（故障事件数量）对估计精度的影响。

实验结果

研究问题

• RQ1 为可靠的随机容量估计需要多少条记录的故障？
• RQ2 截断数据对容量估计的可靠性有何影响？
• RQ3 在不同样本量下，经过校正的最大似然估计器的表现如何？
• RQ4 样本量与容量估计的平均相对误差之间的关系如何？

主要发现

• 至少需要50条记录的故障才能实现可靠性。
• 临时测量的最低推荐故障数为100–200条。
• 超过100–200条故障后，改进效果边际有限。
• 在数据充足时，预期的平均相对误差降至5%以下。