The 2016 safety Final Rule requires states to have access to annual average daily traffic (AADT) for all public paved roads, including non-Federal aid-system (NFAS) roads. The latter account approximately for 75% of the total roadway mileage in the country making it difficult for agencies to collect traffic data on these roads. Many agencies use stratified sampling procedures to develop default AADT estimates for uncounted segments; however, there is limited guidance and information about how to stratify the network effectively. The goal of this paper is to enhance transportation agencies’ ability to improve existing stratification schemes, design new schemes, and ultimately develop more accurate AADT estimates for NFAS roads. The paper presents the results from five pilot studies that validated and compared the performance of current, updated, and new (traditional and decision-tree-based) schemes using readily available data. According to the results, the median absolute percent error of existing AADT estimates, developed by state agencies, ranged between 71% and 120%. Updating these schemes using recent counts resulted in an AADT accuracy improvement of 25%. The best-performing schemes were developed using DTs that improved the AADT accuracy of existing schemes by 41%. Overall, having more strata and very homogenous strata is better than having fewer strata and more samples within each stratum. The analysis revealed that a key to selecting an effective scheme is to determine a critical point, beyond which creating more strata improves the AADT accuracy marginally but increases the required sample size exponentially.

2016 年安全最终规则要求各州获得所有公共铺砌道路的年平均日交通量 (AADT)，包括非联邦援助系统 (NFAS) 道路。后者约占该国道路总里程的 75%，这使得机构难以收集这些道路上的交通数据。许多机构使用分层抽样程序为未计数的细分市场制定默认的 AADT 估计值；然而，关于如何有效地对网络进行分层的指导和信息有限。本文的目标是提高运输机构改进现有分层方案、设计新方案并最终为 NFAS 道路开发更准确的 AADT 估计的能力。本文介绍了五项试点研究的结果，这些研究验证和比较了当前、更新、以及使用现成数据的新（传统的和基于决策树的）方案。根据结果​​，由州机构制定的现有 AADT 估计的中值绝对百分比误差介于 71% 和 120% 之间。使用最近的计数更新这些方案导致 AADT 准确度提高了 25%。性能最佳的方案是使用 DT 开发的，将现有方案的 AADT 精度提高了 41%。总的来说，拥有更多层和非常同质的层比拥有更少的层和每个层中的更多样本要好。分析表明，选择有效方案的关键是确定一个临界点，超过该临界点，创建更多层会略微提高 AADT 的准确性，但会以指数方式增加所需的样本量。