Sample size determination is crucial in experimental design, especially in traffic and transport research. Frequentist statistics require a fixed sample size determined by power analysis, which cannot be adjusted once the experiment starts. Bayesian sample size determination, with proper priors, offers an alternative. Bayesian optional stopping (BOS) allows experiments to stop when statistical targets are met. We introduce predictive Bayesian optional stopping (pBOS), combining BOS with Bayesian rehearsal simulations to predict future data and stop experiments if targets are unlikely to be met within resource constraints. We identified and corrected a bias in predictions using multiple linear regression. pBOS shows up to 118% better cost benefit than traditional BOS and is more efficient than frequentist methods. pBOS allows researchers to, under certain conditions, stop experiments when resources are insufficient or when enough data is collected, optimizing resource use and cost savings.

翻译：样本量确定在实验设计中至关重要，尤其在交通与运输研究中。频率统计方法要求通过功效分析确定固定样本量，一旦实验开始便无法调整。而采用适当先验分布的贝叶斯样本量确定方法提供了替代方案。贝叶斯可选停止（BOS）允许在满足统计目标时终止实验。我们提出预测性贝叶斯可选停止（pBOS）方法，该方法将BOS与贝叶斯预演模拟相结合，通过预测未来数据，在资源约束下目标可能无法达成时提前终止实验。我们通过多元线性回归识别并修正了预测偏差。pBOS相比传统BOS方法可提升高达118%的成本效益，且较频率统计方法更为高效。pBOS使研究者能够在特定条件下，当资源不足或已收集足够数据时终止实验，从而优化资源利用并实现成本节约。