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Determining the optimal number of seasonal adjustment factor groupings when estimating annual average daily traffic and investigating their characteristics

机译：在估算年度平均每日流量并调查其特征时，确定季节性调整因子分组的最佳数量

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摘要

Although cluster analysis is recommended by the US Traffic Monitoring Guide (TMG) to supplement the development of seasonal adjustment factor groupings (SAFGs), the relationships among SAFGs' characteristics remain undiscovered, while the determination of the optimal number of clusters is an ambiguous task exposed to great subjectivity. Statistical indicators provide a mathematical solution by removing engineering judgment without taking into consideration any guidelines or other criteria, necessary for transportation planners to generate 'practical and sensible' groupings. The method examined in this study aims to overcome the above weaknesses incorporating into the methodology a series of statistics, recommendations, and previous research findings. The investigation of the relationships among (1) the within-group variation, (2) the total number of sites, (3) the minimum number of stations within a cluster, (4) the optimal number of clusters, and (5) the geographical size of the groups constitutes the main objectives of this research. According to the results, the cluster variability declines as the available number of stations increases. When the minimum number of stations within a cluster increases, the weighted coefficient of variation inflates as well, with the rate of increase depending on sample size. The average number of automatic traffic recorders per cluster is analogous to the sample size, while the optimal number of clusters varies conversely with the minimum number of stations within a cluster. The application developed for the conduct of the analysis minimizes the computational time needed, while it can be easily implemented by engineers to automate the process recommended by the TMG, enhancing the current state of practice.
机译：尽管《美国交通监视指南》（TMG）建议使用聚类分析来补充季节性调整因子分组（SAFG）的发展，但SAFG特征之间的关系仍未发现，而确定最佳聚类数是一项含糊的任务具有很大的主观性。统计指标通过消除工程判断而不提供运输计划人员生成“实用和明智”分组所必需的任何准则或其他标准，从而提供了一种数学解决方案。本研究中检验的方法旨在克服上述缺点，并将一系列统计数据，建议和先前的研究发现纳入该方法中。研究（1）组内变异，（2）站点总数，（3）集群内最小站点数，（4）最佳集群数和（5）群体的地理大小构成了本研究的主要目标。根据结果​​，聚类变异性随着可用站数的增加而降低。当群集中的最小工作站数增加时，加权变异系数也会随之增加，其增加速率取决于样本大小。每个群集的自动行车记录仪的平均数量类似于样本大小，而群集的最佳数量与群集中的最小工作站数量相​​反。为进行分析而开发的应用程序可最大程度地减少所需的计算时间，同时工程师可以轻松实现该应用程序，以使TMG建议的流程自动化，从而增强当前的实践水平。

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